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\begin_body

\begin_layout Title
Factor Graphs and GTSAM:
\begin_inset Newline newline
\end_inset

A Hands-on Introduction
\end_layout

\begin_layout Author
Frank Dellaert
\begin_inset Newline newline
\end_inset

Technical Report number GT-RIM-CP&R-2014-XXX
\end_layout

\begin_layout Date
September 2014
\end_layout

\begin_layout Standard
\begin_inset CommandInset include
LatexCommand input
filename "common_macros.tex"

\end_inset


\end_layout

\begin_layout Section*
Overview
\end_layout

\begin_layout Standard
In this document I provide a hands-on introduction to both factor graphs
 and GTSAM.
 This is an updated version from the 2012 TR that is tailored to our GTSAM
 3.0 library and beyond.
\end_layout

\begin_layout Standard

\series bold
Factor graphs
\series default
 are graphical models 
\begin_inset CommandInset citation
LatexCommand citep
key "Koller09book"

\end_inset

 that are well suited to modeling complex estimation problems, such as Simultane
ous Localization and Mapping (SLAM) or Structure from Motion (SFM).
 You might be familiar with another often used graphical model, Bayes networks,
 which are directed acyclic graphs.
 A 
\series bold
factor graph, 
\series default
however, is a 
\emph on
bipartite
\emph default
 graph consisting of factors connected to variables.
 The 
\series bold
variables
\series default
 represent the unknown random variables in the estimation problem, whereas
 the 
\series bold
factors
\series default
 represent probabilistic constraints on those variables, derived from measuremen
ts or prior knowledge.
 In the following sections I will illustrate this with examples from both
 robotics and vision.
\end_layout

\begin_layout Standard
The GTSAM toolbox (GTSAM stands for 
\begin_inset Quotes eld
\end_inset

Georgia Tech Smoothing and Mapping
\begin_inset Quotes erd
\end_inset

) toolbox is a BSD-licensed C++ library based on factor graphs, developed
 at the Georgia Institute of Technology by myself, many of my students,
 and collaborators.
 It provides state of the art solutions to the SLAM and SFM problems, but
 can also be used to model and solve both simpler and more complex estimation
 problems.
 It also provides a MATLAB interface which allows for rapid prototype developmen
t, visualization, and user interaction.
\end_layout

\begin_layout Standard
GTSAM exploits sparsity to be computationally efficient.
 Typically measurements only provide information on the relationship between
 a handful of variables, and hence the resulting factor graph will be sparsely
 connected.
 This is exploited by the algorithms implemented in GTSAM to reduce computationa
l complexity.
 Even when graphs are too dense to be handled efficiently by direct methods,
 GTSAM provides iterative methods that are quite efficient regardless.
\end_layout

\begin_layout Standard
You can download the latest version of GTSAM at 
\begin_inset Flex URL
status open

\begin_layout Plain Layout

http://tinyurl.com/gtsam
\end_layout

\end_inset

.
\end_layout

\begin_layout Standard
\begin_inset CommandInset toc
LatexCommand tableofcontents

\end_inset


\end_layout

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\begin_inset Newpage pagebreak
\end_inset


\end_layout

\begin_layout Section
Factor Graphs
\end_layout

\begin_layout Standard
Let us start with a one-page primer on factor graphs, which in no way replaces
 the excellent and detailed reviews by 
\begin_inset CommandInset citation
LatexCommand citet
key "Kschischang01it"

\end_inset

 and 
\begin_inset CommandInset citation
LatexCommand citet
key "Loeliger04spm"

\end_inset

.
 
\end_layout

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placement h
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\begin_layout Plain Layout
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\begin_inset Graphics
	filename images/hmm.pdf
	scale 60
	BoundingBox 40bp 37bp 400bp 150bp
	clip

\end_inset


\begin_inset Caption Standard

\begin_layout Plain Layout
\begin_inset CommandInset label
LatexCommand label
name "fig:unrolledHMM"

\end_inset

An HMM, unrolled over three time-steps, represented by a Bayes net.
 
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Standard
\begin_inset ERT
status open

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\backslash
noindent
\end_layout

\end_inset

 Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:unrolledHMM"

\end_inset

 shows the 
\series bold
Bayes network
\series default
 for a hidden Markov model (HMM) over three time steps.
 In a Bayes net, each node is associated with a conditional density: the
 top Markov chain encodes the prior 
\begin_inset Formula $P(X_{1})$
\end_inset

 and transition probabilities 
\begin_inset Formula $P(X_{2}|X_{1})$
\end_inset

 and 
\begin_inset Formula $P(X_{3}|X_{2})$
\end_inset

, whereas measurements 
\begin_inset Formula $Z_{t}$
\end_inset

 depend only on the state 
\begin_inset Formula $X_{t}$
\end_inset

, modeled by conditional densities 
\begin_inset Formula $P(Z_{t}|X_{t})$
\end_inset

.
 Given known measurements 
\begin_inset Formula $z_{1}$
\end_inset

, 
\begin_inset Formula $z_{2}$
\end_inset

 and 
\begin_inset Formula $z_{3}$
\end_inset

 we are interested in the hidden state sequence 
\begin_inset Formula $(X_{1},X_{2},X_{3})$
\end_inset

 that maximizes the posterior probability 
\begin_inset Formula $P(X_{1},X_{2},X_{3}|Z_{1}=z_{1},Z_{2}=z_{2},Z_{3}=z_{3})$
\end_inset

.
 Since the measurements 
\begin_inset Formula $Z_{1}$
\end_inset

, 
\begin_inset Formula $Z_{2}$
\end_inset

, and 
\begin_inset Formula $Z_{3}$
\end_inset

 are 
\emph on
known
\emph default
, the posterior is proportional to the product of six 
\series bold
factors
\series default
, three of which derive from the the Markov chain, and three likelihood
 factors defined as 
\begin_inset Formula $L(X_{t};z)\propto P(Z_{t}=z|X_{t})$
\end_inset

:
\begin_inset Formula 
\[
P(X_{1},X_{2},X_{3}|Z_{1},Z_{2},Z_{3})\propto P(X_{1})P(X_{2}|X_{1})P(X_{3}|X_{2})L(X_{1};z_{1})L(X_{2};z_{2})L(X_{3};z_{3})
\]

\end_inset


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placement H
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	filename images/hmm-FG.pdf
	scale 60
	BoundingBox 30bp 40bp 340bp 130bp
	clip

\end_inset


\begin_inset Caption Standard

\begin_layout Plain Layout
\begin_inset CommandInset label
LatexCommand label
name "fig:HMM-FG"

\end_inset

An HMM with observed measurements, unrolled over time, represented as a
 factor graph.
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

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\backslash
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\end_inset

 This motivates a different graphical model, a 
\series bold
factor graph
\series default
, in which we only represent the unknown variables 
\begin_inset Formula $X_{1}$
\end_inset

, 
\begin_inset Formula $X_{2}$
\end_inset

, and 
\begin_inset Formula $X_{3}$
\end_inset

, connected to factors that encode probabilistic information on them, as
 in Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:HMM-FG"

\end_inset

.
 To do maximum a-posteriori (MAP) inference, we then maximize the product
 
\begin_inset Formula 
\[
f(X_{1},X_{2},X_{3})=\prod f_{i}(\mathcal{X}_{i})
\]

\end_inset

i.e., the value of the factor graph.
 It should be clear from the figure that the connectivity of a factor graph
 encodes, for each factor 
\begin_inset Formula $f_{i}$
\end_inset

, which subset of variables 
\begin_inset Formula $\mathcal{X}_{i}$
\end_inset

 it depends on.
 In the examples below, we use factor graphs to model more complex MAP inference
 problems in robotics.
\end_layout

\begin_layout Section
\begin_inset CommandInset label
LatexCommand label
name "sec:Robot-Localization"

\end_inset

Modeling Robot Motion
\end_layout

\begin_layout Subsection
Modeling with Factor Graphs
\end_layout

\begin_layout Standard
Before diving into a SLAM example, let us consider the simpler problem of
 modeling robot motion.
 This can be done with a 
\emph on
continuous
\emph default
 Markov chain, and provides a gentle introduction to GTSAM.
\end_layout

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placement h
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	scale 80
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	clip

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\begin_inset Caption Standard

\begin_layout Plain Layout
\begin_inset CommandInset label
LatexCommand label
name "fig:OdometryFG"

\end_inset

Factor graph for robot localization.
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

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\backslash
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\end_inset

 The factor graph for a simple example is shown in Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:OdometryFG"

\end_inset

.
 There are three variables 
\begin_inset Formula $x_{1}$
\end_inset

, 
\begin_inset Formula $x_{2}$
\end_inset

, and 
\begin_inset Formula $x_{3}$
\end_inset

 which represent the poses of the robot over time, rendered in the figure
 by the open-circle variable nodes.
 In this example, we have one 
\series bold
unary factor
\series default
 
\begin_inset Formula $f_{0}(x_{1})$
\end_inset

 on the first pose 
\begin_inset Formula $x_{1}$
\end_inset

 that encodes our prior knowledge about 
\begin_inset Formula $x_{1}$
\end_inset

, and two 
\series bold
binary factors
\series default
 that relate successive poses, respectively 
\begin_inset Formula $f_{1}(x_{1},x_{2};o_{1})$
\end_inset

 and 
\begin_inset Formula $f_{2}(x_{2},x_{3};o_{2})$
\end_inset

, where 
\begin_inset Formula $o_{1}$
\end_inset

 and 
\begin_inset Formula $o_{2}$
\end_inset

 represent odometry measurements.
\end_layout

\begin_layout Subsection
Creating a Factor Graph
\end_layout

\begin_layout Standard
The following C++ code, included in GTSAM as an example, creates the factor
 graph in Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:OdometryFG"

\end_inset

:
\end_layout

\begin_layout Standard
\begin_inset CommandInset include
LatexCommand lstinputlisting
filename "Code/OdometryExample.cpp"
lstparams "aboveskip=10pt,basicstyle={\\ttfamily\\small},caption={Excerpt from examples/OdometryExample.cpp},captionpos=b,frame=single,identifierstyle={\\bfseries},label={listing:OdometryExample},language={C++},numbers=left"

\end_inset


\end_layout

\begin_layout Standard
\begin_inset ERT
status open

\begin_layout Plain Layout


\backslash
noindent
\end_layout

\end_inset

 Above, line 2 creates an empty factor graph.
 We then add the factor 
\begin_inset Formula $f_{0}(x_{1})$
\end_inset

 on lines 5-8 as an instance of 
\series bold
\emph on
PriorFactor<T>
\series default
\emph default
, a templated class provided in the slam subfolder, with 
\series bold
\emph on
T=Pose2
\series default
\emph default
.
 Its constructor takes a variable 
\series bold
\emph on
Key
\series default
\emph default
 (in this case 1), a mean of type 
\series bold
\emph on
Pose2,
\series default
\emph default
 created on Line 5, and a noise model for the prior density.
 We provide a diagonal Gaussian of type 
\series bold
\emph on
noiseModel::Diagonal
\series default
\emph default
 by specifying three standard deviations in line 7, respectively 30 cm.
\begin_inset space ~
\end_inset

on the robot's position, and 0.1 radians on the robot's orientation.
 Note that the 
\series bold
\emph on
Sigmas
\series default
\emph default
 constructor returns a shared pointer, anticipating that typically the same
 noise models are used for many different factors.
\end_layout

\begin_layout Standard
Similarly, odometry measurements are specified as 
\series bold
\emph on
Pose2
\series default
\emph default
 on line 11, with a slightly different noise model defined on line 12-13.
 We then add the two factors 
\begin_inset Formula $f_{1}(x_{1},x_{2};o_{1})$
\end_inset

 and 
\begin_inset Formula $f_{2}(x_{2},x_{3};o_{2})$
\end_inset

 on lines 14-15, as instances of yet another templated class, 
\series bold
\emph on
BetweenFactor<T>
\series default
\emph default
, again with 
\series bold
\emph on
T=Pose2
\series default
\emph default
.
\end_layout

\begin_layout Standard
When running the example (
\emph on
make OdometryExample.run
\emph default
 on the command prompt), it will print out the factor graph as follows:
\family typewriter
\size small

\begin_inset CommandInset include
LatexCommand verbatiminput
filename "Code/OdometryOutput1.txt"

\end_inset


\end_layout

\begin_layout Subsection
Factor Graphs versus Values
\end_layout

\begin_layout Standard
At this point it is instructive to emphasize two important design ideas
 underlying GTSAM:
\end_layout

\begin_layout Enumerate
The factor graph and its embodiment in code specify the joint probability
 distribution 
\begin_inset Formula $P(X|Z)$
\end_inset

 over the 
\emph on
entire
\emph default
 trajectory 
\begin_inset Formula $X\define\{x_{1},x_{2},x_{3}\}$
\end_inset

 of the robot, rather than just the last pose.
 This 
\emph on
smoothing
\emph default
 view of the world gives GTSAM its name: 
\begin_inset Quotes eld
\end_inset

smoothing and mapping
\begin_inset Quotes erd
\end_inset

.
 Later in this document we will talk about how we can also use GTSAM to
 do filtering (which you often do 
\emph on
not
\emph default
 want to do) or incremental inference (which we do all the time).
\end_layout

\begin_layout Enumerate
A factor graph in GTSAM is just the specification of the probability density
 
\begin_inset Formula $P(X|Z)$
\end_inset

, and the corresponding 
\series bold
\emph on
FactorGraph
\series default
\emph default
 class and its derived classes do not ever contain a 
\begin_inset Quotes eld
\end_inset

solution
\begin_inset Quotes erd
\end_inset

.
 Rather, there is a separate type 
\series bold
\emph on
Values
\series default
\emph default
 that is used to specify specific values for (in this case) 
\begin_inset Formula $x_{1}$
\end_inset

, 
\begin_inset Formula $x_{2}$
\end_inset

, and 
\begin_inset Formula $x_{3}$
\end_inset

, which can then be used to evaluate the probability (or, more commonly,
 the error) associated with particular values.
\end_layout

\begin_layout Standard
The latter point is often a point of confusion with beginning users of GTSAM.
 It helps to remember that when designing GTSAM we took a functional approach
 of classes corresponding to mathematical objects, which are usually immutable.
 You should think of a factor graph as a 
\emph on
function
\emph default
 to be applied to values -as the notation 
\begin_inset Formula $f(X)\propto P(X|Z)$
\end_inset

 implies- rather than as an object to be modified.
\end_layout

\begin_layout Subsection
Non-linear Optimization in GTSAM
\end_layout

\begin_layout Standard
The listing below creates a 
\series bold
\emph on
Values
\series default
\emph default
 instance, and uses it as the initial estimate to find the maximum a-posteriori
 (MAP) assignment for the trajectory 
\begin_inset Formula $X$
\end_inset

:
\end_layout

\begin_layout Standard
\begin_inset CommandInset include
LatexCommand lstinputlisting
filename "Code/OdometryOptimize.cpp"
lstparams "aboveskip=10pt,basicstyle={\\ttfamily\\small},caption={Excerpt from examples/OdometryExample.cpp},captionpos=b,frame=single,identifierstyle={\\bfseries},label={listing:OdometryOptimize},language={C++},numbers=left"

\end_inset


\end_layout

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\backslash
noindent
\end_layout

\end_inset

 Lines 2-5 in Listing 
\begin_inset CommandInset ref
LatexCommand ref
reference "listing:OdometryOptimize"

\end_inset

 create the initial estimate, and on line 8 we create a non-linear Levenberg-Mar
quardt style optimizer, and call 
\series bold
\emph on
optimize
\series default
\emph default
 using default parameter settings.
 The reason why GTSAM needs to perform non-linear optimization is because
 the odometry factors 
\begin_inset Formula $f_{1}(x_{1},x_{2};o_{1})$
\end_inset

 and 
\begin_inset Formula $f_{2}(x_{2},x_{3};o_{2})$
\end_inset

 are non-linear, as they involve the orientation of the robot.
 This also explains why the factor graph we created in Listing 
\begin_inset CommandInset ref
LatexCommand ref
reference "listing:OdometryExample"

\end_inset

 is of type 
\series bold
\emph on
NonlinearFactorGraph
\series default
\emph default
.
 The optimization class linearizes this graph, possibly multiple times,
 to minimize the non-linear squared error specified by the factors.
\end_layout

\begin_layout Standard
The relevant output from running the example is as follows:
\family typewriter
\size small

\begin_inset CommandInset include
LatexCommand verbatiminput
filename "Code/OdometryOutput2.txt"

\end_inset


\end_layout

\begin_layout Standard
\begin_inset ERT
status open

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\backslash
noindent
\end_layout

\end_inset

 It can be seen that, subject to very small tolerance, the ground truth
 solution 
\begin_inset Formula $x_{1}=(0,0,0)$
\end_inset

, 
\begin_inset Formula $x_{2}=(2,0,0)$
\end_inset

, and 
\begin_inset Formula $x_{3}=(4,0,0)$
\end_inset

 is recovered.
\end_layout

\begin_layout Subsection
\begin_inset CommandInset label
LatexCommand label
name "subsec:Full-Posterior-Inference"

\end_inset

Full Posterior Inference
\end_layout

\begin_layout Standard
GTSAM can also be used to calculate the covariance matrix for each pose
 after incorporating the information from all measurements 
\begin_inset Formula $Z$
\end_inset

.
 Recognizing that the factor graph encodes the 
\series bold
posterior density
\series default
 
\begin_inset Formula $P(X|Z)$
\end_inset

, the mean 
\begin_inset Formula $\mu$
\end_inset

 together with the covariance 
\begin_inset Formula $\Sigma$
\end_inset

 for each pose 
\begin_inset Formula $x$
\end_inset

 approximate the 
\series bold
marginal posterior density
\series default
 
\begin_inset Formula $P(x|Z)$
\end_inset

.
 Note that this is just an approximation, as even in this simple case the
 odometry factors are actually non-linear in their arguments, and GTSAM
 only computes a Gaussian approximation to the true underlying posterior.
\end_layout

\begin_layout Standard
The following C++ code will recover the posterior marginals:
\end_layout

\begin_layout Standard
\begin_inset CommandInset include
LatexCommand lstinputlisting
filename "Code/OdometryMarginals.cpp"
lstparams "aboveskip=10pt,basicstyle={\\ttfamily\\small},captionpos=b,frame=single,identifierstyle={\\bfseries},language={C++},numbers=left,caption={Excerpt from examples/OdometryExample.cpp},label={listing:OdometryMarginals}"

\end_inset

The relevant output from running the example is as follows:
\size footnotesize

\begin_inset CommandInset include
LatexCommand verbatiminput
filename "Code/OdometryOutput3.txt"

\end_inset


\end_layout

\begin_layout Standard
\begin_inset ERT
status open

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\backslash
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\end_layout

\end_inset

 What we see is that the marginal covariance 
\begin_inset Formula $P(x_{1}|Z)$
\end_inset

 on 
\begin_inset Formula $x_{1}$
\end_inset

 is simply the prior knowledge on 
\begin_inset Formula $x_{1}$
\end_inset

, but as the robot moves the uncertainty in all dimensions grows without
 bound, and the 
\begin_inset Formula $y$
\end_inset

 and 
\begin_inset Formula $\theta$
\end_inset

 components of the pose become (positively) correlated.
\end_layout

\begin_layout Standard
An important fact to note when interpreting these numbers is that covariance
 matrices are given in 
\emph on
relative
\emph default
 coordinates, not absolute coordinates.
 This is because internally GTSAM optimizes for a change with respect to
 a linearization point, as do all nonlinear optimization libraries.
\end_layout

\begin_layout Section
Robot Localization
\end_layout

\begin_layout Subsection
Unary Measurement Factors
\end_layout

\begin_layout Standard
In this section we add measurements to the factor graph that will help us
 actually 
\emph on
localize
\emph default
 the robot over time.
 The example also serves as a tutorial on creating new factor types.
\end_layout

\begin_layout Standard
\begin_inset Float figure
placement h
wide false
sideways false
status open

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename images/FactorGraph2.pdf
	scale 80
	BoundingBox 70bp 550bp 300bp 630bp
	clip

\end_inset


\begin_inset Caption Standard

\begin_layout Plain Layout
\begin_inset CommandInset label
LatexCommand label
name "fig:LocalizationFG"

\end_inset

Robot localization factor graph with unary measurement factors at each time
 step.
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Standard
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\backslash
noindent
\end_layout

\end_inset

 In particular, we use 
\series bold
unary measurement factors
\series default
 to handle external measurements.
 The example from Section 
\begin_inset CommandInset ref
LatexCommand ref
reference "sec:Robot-Localization"

\end_inset

 is not very useful on a real robot, because it only contains factors correspond
ing to odometry measurements.
 These are imperfect and will lead to quickly accumulating uncertainty on
 the last robot pose, at least in the absence of any external measurements
 (see Section 
\begin_inset CommandInset ref
LatexCommand ref
reference "subsec:Full-Posterior-Inference"

\end_inset

).
 Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:LocalizationFG"

\end_inset

 shows a new factor graph where the prior 
\begin_inset Formula $f_{0}(x_{1})$
\end_inset

 is omitted and instead we added three unary factors 
\begin_inset Formula $f_{1}(x_{1};z_{1})$
\end_inset

, 
\begin_inset Formula $f_{2}(x_{2};z_{2})$
\end_inset

, and 
\begin_inset Formula $f_{3}(x_{3};z_{3})$
\end_inset

, one for each localization measurement 
\begin_inset Formula $z_{t}$
\end_inset

, respectively.
 Such unary factors are applicable for measurements 
\begin_inset Formula $z_{t}$
\end_inset

 that depend 
\emph on
only
\emph default
 on the current robot pose, e.g., GPS readings, correlation of a laser range-finde
r in a pre-existing map, or indeed the presence of absence of ceiling lights
 (see 
\begin_inset CommandInset citation
LatexCommand citet
key "Dellaert99b"

\end_inset

 for that amusing example).
\end_layout

\begin_layout Subsection
Defining Custom Factors
\end_layout

\begin_layout Standard
In GTSAM, you can create custom unary factors by deriving a new class from
 the built-in class 
\series bold
\emph on
NoiseModelFactor1<T>
\series default
\emph default
, which implements a unary factor corresponding to a measurement likelihood
 with a Gaussian noise model,
\begin_inset Formula 
\[
L(q;m)=exp\left\{ -\frac{1}{2}\SqrMah{h(q)}{m}{\Sigma}\right\} \define f(q)
\]

\end_inset

where 
\begin_inset Formula $m$
\end_inset

 is the measurement, 
\begin_inset Formula $q$
\end_inset

 is the unknown variable, 
\begin_inset Formula $h(q)$
\end_inset

 is a (possibly nonlinear) measurement function, and 
\begin_inset Formula $\Sigma$
\end_inset

 is the noise covariance.
 Note that 
\begin_inset Formula $m$
\end_inset

 is considered 
\emph on
known
\emph default
 above, and the likelihood 
\begin_inset Formula $L(q;m)$
\end_inset


\begin_inset Note Note
status open

\begin_layout Plain Layout
 of 
\begin_inset Formula $q$
\end_inset

 given 
\begin_inset Formula $m$
\end_inset


\end_layout

\end_inset

 will only ever be evaluated as a function of 
\begin_inset Formula $q$
\end_inset

, which explains why it is a unary factor 
\begin_inset Formula $f(q)$
\end_inset

.
 It is always the unknown variable 
\begin_inset Formula $q$
\end_inset

 that is either likely or unlikely, given the measurement.
 
\end_layout

\begin_layout Standard

\series bold
Note: 
\series default
many people get this backwards, often misled by the conditional density
 notation 
\begin_inset Formula $P(m|q)$
\end_inset

.
 In fact, the likelihood 
\begin_inset Formula $L(q;m)$
\end_inset

 is 
\emph on
defined
\emph default
 as any function of 
\begin_inset Formula $q$
\end_inset

 proportional to 
\begin_inset Formula $P(m|q)$
\end_inset

.
\end_layout

\begin_layout Standard
Listing 
\begin_inset CommandInset ref
LatexCommand vref
reference "listing:LocalizationFactor"

\end_inset

 shows an example on how to define the custom factor class 
\series bold
\emph on
UnaryFactor
\series default
\emph default
 which implements a 
\begin_inset Quotes eld
\end_inset

GPS-like
\begin_inset Quotes erd
\end_inset

 measurement likelihood:
\end_layout

\begin_layout Standard
\begin_inset CommandInset include
LatexCommand lstinputlisting
filename "Code/LocalizationFactor.cpp"
lstparams "aboveskip=10pt,basicstyle={\\ttfamily\\small},captionpos=b,frame=single,identifierstyle={\\bfseries},language={C++},numbers=left,caption={Excerpt from examples/LocalizationExample.cpp},label={listing:LocalizationFactor}"

\end_inset


\end_layout

\begin_layout Standard
\begin_inset ERT
status open

\begin_layout Plain Layout


\backslash
noindent
\end_layout

\end_inset

 In defining the derived class on line 1, we provide the template argument
 
\series bold
\emph on
Pose2
\series default
\emph default
 to indicate the type of the variable 
\begin_inset Formula $q$
\end_inset

, whereas the measurement is stored as the instance variables 
\series bold
\emph on
mx_
\series default
\emph default
 and 
\series bold
\emph on
my_
\series default
\emph default
, defined on line 2.
 The constructor on lines 5-6 simply passes on the variable key 
\begin_inset Formula $j$
\end_inset

 and the noise model to the superclass, and stores the measurement values
 provided.
 The most important function to has be implemented by every factor class
 is 
\series bold
\emph on
evaluateError
\series default
\emph default
, which should return 
\begin_inset Formula 
\[
E(q)\define h(q)-m
\]

\end_inset

which is done on line 12.
 Importantly, because we want to use this factor for nonlinear optimization
 (see e.g., 
\begin_inset CommandInset citation
LatexCommand citealt
key "Dellaert06ijrr"

\end_inset

 for details), whenever the optional argument 
\begin_inset Formula $H$
\end_inset

 is provided, a 
\series bold
\emph on
Matrix
\series default
\emph default
 reference, the function should assign the 
\series bold
Jacobian
\series default
 of 
\begin_inset Formula $h(q)$
\end_inset

 to it, evaluated at the provided value for 
\begin_inset Formula $q$
\end_inset

.
 This is done for this example on line 11.
 In this case, the Jacobian of the 2-dimensional function 
\begin_inset Formula $h$
\end_inset

, which just returns the position of the robot, 
\begin_inset Formula 
\[
h(q)=\left[\begin{array}{c}
q_{x}\\
q_{y}
\end{array}\right]
\]

\end_inset

 with respect the 3-dimensional pose 
\begin_inset Formula $q=\left(q_{x},q_{y},q_{\theta}\right)$
\end_inset

, yields the following simple 
\begin_inset Formula $2\times3$
\end_inset

 matrix in tangent space which is the same the as the rotation matrix:
\end_layout

\begin_layout Standard
\begin_inset Formula 
\[
H=\left[\begin{array}{ccc}
\cos(q_{\theta}) & -\sin(q_{\theta}) & 0\\
\sin(q_{\theta}) & \cos(q_{\theta}) & 0
\end{array}\right]
\]

\end_inset


\end_layout

\begin_layout Subsection
Using Custom Factors
\end_layout

\begin_layout Standard
The following C++ code fragment illustrates how to create and add custom
 factors to a factor graph:
\end_layout

\begin_layout Standard
\begin_inset CommandInset include
LatexCommand lstinputlisting
filename "Code/LocalizationExample2.cpp"
lstparams "aboveskip=10pt,basicstyle={\\ttfamily\\small},caption={Excerpt from examples/LocalizationExample.cpp},captionpos=b,frame=single,identifierstyle={\\bfseries},label={listing:LocalizationExample2},language={C++},numbers=left"

\end_inset


\begin_inset ERT
status open

\begin_layout Plain Layout


\backslash
noindent
\end_layout

\end_inset

 In Listing 
\begin_inset CommandInset ref
LatexCommand vref
reference "listing:LocalizationExample2"

\end_inset

, we create the noise model on line 2-3, which now specifies two standard
 deviations on the measurements 
\begin_inset Formula $m_{x}$
\end_inset

 and 
\begin_inset Formula $m_{y}$
\end_inset

.
 On lines 4-6 we create 
\series bold
\emph on
shared_ptr
\series default
\emph default
 versions of three newly created 
\series bold
\emph on
UnaryFactor
\series default
\emph default
 instances, and add them to graph.
 GTSAM uses shared pointers to refer to factors in factor graphs, and 
\series bold
\emph on
boost::make_shared
\series default
\emph default
 is a convenience function to simultaneously construct a class and create
 a 
\series bold
\emph on
shared_ptr
\series default
\emph default
 to it.
 
\begin_inset Note Note
status collapsed

\begin_layout Plain Layout
and on lines 4-6 we add three newly created 
\series bold
\emph on
UnaryFactor
\series default
\emph default
 instances to the graph.
\end_layout

\end_inset

 We obtain the factor graph from Figure 
\begin_inset CommandInset ref
LatexCommand vref
reference "fig:LocalizationFG"

\end_inset

.
 
\family typewriter
\size small

\begin_inset Note Note
status collapsed

\begin_layout Plain Layout
The relevant output from running the example is as follows:
\family typewriter
\size small

\begin_inset CommandInset include
LatexCommand verbatiminput
filename "Code/LocalizationOutput4.txt"

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Subsection
Full Posterior Inference
\end_layout

\begin_layout Standard
The three GPS factors are enough to fully constrain all unknown poses and
 tie them to a 
\begin_inset Quotes eld
\end_inset

global
\begin_inset Quotes erd
\end_inset

 reference frame, including the three unknown orientations.
 If not, GTSAM would have exited with a singular matrix exception.
 The marginals can be recovered exactly as in Section 
\begin_inset CommandInset ref
LatexCommand ref
reference "subsec:Full-Posterior-Inference"

\end_inset

, and the solution and marginal covariances are now given by the following:
\size footnotesize

\begin_inset CommandInset include
LatexCommand verbatiminput
filename "Code/LocalizationOutput5.txt"

\end_inset


\end_layout

\begin_layout Standard
Comparing this with the covariance matrices in Section 
\begin_inset CommandInset ref
LatexCommand ref
reference "subsec:Full-Posterior-Inference"

\end_inset

, we can see that the uncertainty no longer grows without bounds as measurement
 uncertainty accumulates.
 Instead, the 
\begin_inset Quotes eld
\end_inset

GPS
\begin_inset Quotes erd
\end_inset

 measurements more or less constrain the poses evenly, as expected.
\end_layout

\begin_layout Standard
\begin_inset Float figure
placement h
wide false
sideways false
status open

\begin_layout Plain Layout
\align center
\begin_inset Float figure
wide false
sideways false
status open

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename images/Odometry.pdf
	width 80text%
	BoundingBox 70bp 310bp 525bp 500bp
	clip

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout
Odometry marginals
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Plain Layout
\align center
\begin_inset space ~
\end_inset


\end_layout

\begin_layout Plain Layout
\align center
\begin_inset Float figure
wide false
sideways false
status open

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename images/Localization.pdf
	width 80text%
	BoundingBox 70bp 310bp 525bp 500bp
	clip

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout
Localization Marginals
\end_layout

\end_inset


\end_layout

\end_inset


\begin_inset Caption Standard

\begin_layout Plain Layout
\begin_inset CommandInset label
LatexCommand label
name "fig:CompareMarginals"

\end_inset

Comparing the marginals resulting from the 
\begin_inset Quotes eld
\end_inset

odometry
\begin_inset Quotes erd
\end_inset

 factor graph in Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:OdometryFG"

\end_inset

 and the 
\begin_inset Quotes eld
\end_inset

localization
\begin_inset Quotes erd
\end_inset

 factor graph in Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:LocalizationFG"

\end_inset

.
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Standard
It helps a lot when we view this graphically, as in Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:CompareMarginals"

\end_inset

, where I show the marginals on position as covariance ellipses that contain
 68.26% of all probability mass.
 For the odometry marginals, it is immediately apparent from the figure
 that (1) the uncertainty on pose keeps growing, and (2) the uncertainty
 on angular odometry translates into increasing uncertainty on y.
 The localization marginals, in contrast, are constrained by the unary factors
 and are all much smaller.
 In addition, while less apparent, the uncertainty on the middle pose is
 actually smaller as it is constrained by odometry from two sides.
\end_layout

\begin_layout Standard
You might now be wondering how we produced these figures.
 The answer is via the MATLAB interface of GTSAM, which we will demonstrate
 in the next section.
\end_layout

\begin_layout Standard
\begin_inset Newpage pagebreak
\end_inset


\end_layout

\begin_layout Section
\begin_inset CommandInset label
LatexCommand label
name "sec:Pose2SLAM"

\end_inset


\begin_inset CommandInset label
LatexCommand label
name "sec:WithMarginals"

\end_inset

PoseSLAM
\end_layout

\begin_layout Subsection
Loop Closure Constraints
\end_layout

\begin_layout Standard
The simplest instantiation of a SLAM problem is 
\series bold
PoseSLAM
\series default
, which avoids building an explicit map of the environment.
 The goal of SLAM is to simultaneously localize a robot and map the environment
 given incoming sensor measurements 
\begin_inset CommandInset citation
LatexCommand citep
key "DurrantWhyte06ram"

\end_inset

.
 Besides wheel odometry, one of the most popular sensors for robots moving
 on a plane is a 2D laser-range finder, which provides both odometry constraints
 between successive poses, and loop-closure constraints when the robot re-visits
 a previously explored part of the environment.
\end_layout

\begin_layout Standard
\begin_inset Float figure
placement h
wide false
sideways false
status open

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename images/FactorGraph3.pdf
	scale 80
	BoundingBox 40bp 585bp 330bp 710bp
	clip

\end_inset


\begin_inset Caption Standard

\begin_layout Plain Layout
\begin_inset CommandInset label
LatexCommand label
name "fig:Pose2SLAM"

\end_inset

Factor graph for PoseSLAM.
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Standard
\begin_inset ERT
status open

\begin_layout Plain Layout


\backslash
noindent
\end_layout

\end_inset

 A factor graph example for PoseSLAM is shown in Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:Pose2SLAM"

\end_inset

.
 The following C++ code, included in GTSAM as an example, creates this factor
 graph in code: 
\end_layout

\begin_layout Standard
\begin_inset CommandInset include
LatexCommand lstinputlisting
filename "Code/Pose2SLAMExample.cpp"
lstparams "aboveskip=10pt,basicstyle={\\ttfamily\\small},caption={Excerpt from examples/Pose2SLAMExample.cpp},captionpos=b,frame=single,identifierstyle={\\bfseries},label={listing:Pose2SLAMExample},language={C++},numbers=left"

\end_inset


\end_layout

\begin_layout Standard
\begin_inset ERT
status open

\begin_layout Plain Layout


\backslash
noindent
\end_layout

\end_inset

 As before, lines 1-4 create a nonlinear factor graph and add the unary
 factor 
\begin_inset Formula $f_{0}(x_{1})$
\end_inset

.
 As the robot travels through the world, it creates binary factors 
\begin_inset Formula $f_{t}(x_{t},x_{t+1})$
\end_inset

 corresponding to odometry, added to the graph in lines 6-12 (Note that
 M_PI_2 refers to pi/2).
 But line 15 models a different event: a 
\series bold
loop closure
\series default
.
 For example, the robot might recognize the same location using vision or
 a laser range finder, and calculate the geometric pose constraint to when
 it first visited this location.
 This is illustrated for poses 
\begin_inset Formula $x_{5}$
\end_inset

 and 
\begin_inset Formula $x_{2}$
\end_inset

, and generates the (red) loop closing factor 
\begin_inset Formula $f_{5}(x_{5},x_{2})$
\end_inset

.
\end_layout

\begin_layout Standard
\begin_inset Float figure
placement h
wide false
sideways false
status open

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename images/example1.pdf
	width 80text%
	BoundingBox 30bp 170bp 610bp 630bp
	clip

\end_inset


\begin_inset Caption Standard

\begin_layout Plain Layout
\begin_inset CommandInset label
LatexCommand label
name "fig:example"

\end_inset

The result of running optimize on the factor graph in Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:Pose2SLAM"

\end_inset

.
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Standard
We can optimize this factor graph as before, by creating an initial estimate
 of type 
\series bold
\emph on
Values
\series default
\emph default
, and creating and running an optimizer.
 The result is shown graphically in Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:example"

\end_inset

, along with covariance ellipses shown in green.
 These covariance ellipses in 2D indicate the marginal over position, over
 all possible orientations, and show the area which contain 68.26% of the
 probability mass (in 1D this would correspond to one standard deviation).
 The graph shows in a clear manner that the uncertainty on pose 
\begin_inset Formula $x_{5}$
\end_inset

 is now much less than if there would be only odometry measurements.
 The pose with the highest uncertainty, 
\begin_inset Formula $x_{4}$
\end_inset

, is the one furthest away from the unary constraint 
\begin_inset Formula $f_{0}(x_{1})$
\end_inset

, which is the only factor tying the graph to a global coordinate frame.
\end_layout

\begin_layout Standard
The figure above was created using an interface that allows you to use GTSAM
 from within MATLAB, which provides for visualization and rapid development.
 We discuss this next.
\end_layout

\begin_layout Subsection
Using the MATLAB Interface
\end_layout

\begin_layout Standard
A large subset of the GTSAM functionality can be accessed through wrapped
 classes from within MATLAB 
\begin_inset Foot
status open

\begin_layout Plain Layout
GTSAM also allows you to wrap your own custom-made classes, although this
 is outside the scope of this manual
\end_layout

\end_inset

.
 The following code excerpt is the MATLAB equivalent of the C++ code in
 Listing 
\begin_inset CommandInset ref
LatexCommand ref
reference "listing:Pose2SLAMExample"

\end_inset

:
\begin_inset CommandInset include
LatexCommand lstinputlisting
filename "Code/Pose2SLAMExample.m"
lstparams "aboveskip=10pt,basicstyle={\\ttfamily\\small},captionpos=b,frame=single,identifierstyle={\\bfseries},language={Matlab},numbers=left,caption={Excerpt from matlab/gtsam\\_examples/Pose2SLAMExample.m},label={listing:Pose2SLAMExample-MATLAB}"

\end_inset


\end_layout

\begin_layout Standard
\begin_inset ERT
status open

\begin_layout Plain Layout


\backslash
noindent
\end_layout

\end_inset

 Note that the code is almost identical, although there are a few syntax
 and naming differences:
\end_layout

\begin_layout Itemize
Objects are created by calling a constructor instead of allocating them
 on the heap.
\end_layout

\begin_layout Itemize
Namespaces are done using dot notation, i.e., 
\series bold
\emph on
noiseModel::Diagonal::SigmasClasses
\series default
\emph default
 becomes 
\series bold
\emph on
noiseModel.Diagonal.Sigmas
\series default
\emph default
.
\end_layout

\begin_layout Itemize

\series bold
\emph on
Vector
\series default
\emph default
 and 
\series bold
\emph on
Matrix
\series default
\emph default
 classes in C++ are just vectors/matrices in MATLAB.
\end_layout

\begin_layout Itemize
As templated classes do not exist in MATLAB, these have been hardcoded in
 the GTSAM interface, e.g., 
\series bold
\emph on
PriorFactorPose2
\series default
\emph default
 corresponds to the C++ class 
\series bold
\emph on
PriorFactor<Pose2>
\series default
\emph default
, etc.
\end_layout

\begin_layout Standard
After executing the code, you can call 
\emph on
whos 
\emph default
on the MATLAB command prompt to see the objects created.
 Note that the indicated 
\emph on
Class
\emph default
 corresponds to the wrapped C++ classes:
\size small

\begin_inset CommandInset include
LatexCommand verbatiminput
filename "Code/whos.txt"

\end_inset


\size default

\begin_inset ERT
status open

\begin_layout Plain Layout


\backslash
noindent
\end_layout

\end_inset

 In addition, any GTSAM object can be examined in detail, yielding identical
 output to C++:
\size small
 
\begin_inset CommandInset include
LatexCommand verbatiminput
filename "Code/print.txt"

\end_inset


\size default

\begin_inset ERT
status open

\begin_layout Plain Layout


\backslash
noindent
\end_layout

\end_inset

 And it does not stop there: we can also call some of the functions defined
 for factor graphs.
 E.g.,
\end_layout

\begin_layout Standard

\size small
\begin_inset CommandInset include
LatexCommand verbatiminput
filename "Code/calls.txt"

\end_inset


\size default

\begin_inset ERT
status open

\begin_layout Plain Layout


\backslash
noindent
\end_layout

\end_inset

 computes the sum-squared error 
\begin_inset Formula $\frac{1}{2}\sum_{i}\SqrMah{h_{i}(X_{i})}{z_{i}}{\Sigma}$
\end_inset

 before and after optimization.
\end_layout

\begin_layout Subsection
Reading and Optimizing Pose Graphs 
\end_layout

\begin_layout Standard
\begin_inset Float figure
placement h
wide false
sideways false
status open

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename images/w100-result.pdf
	width 80text%
	BoundingBox 30bp 170bp 610bp 630bp
	clip

\end_inset


\begin_inset Caption Standard

\begin_layout Plain Layout
\begin_inset CommandInset label
LatexCommand label
name "fig:w100"

\end_inset

MATLAB plot of small Manhattan world example with 100 poses (due to Ed Olson).
 The initial estimate is shown in green.
 The optimized trajectory, with covariance ellipses, in blue.
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Standard
The ability to work in MATLAB adds a much quicker development cycle, and
 effortless graphical output.
 The optimized trajectory in Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:w100"

\end_inset

 was produced by the code below, in which 
\emph on
load2D
\emph default
 reads TORO files.
 To see how plotting is done, refer to the full source code.
\begin_inset CommandInset include
LatexCommand lstinputlisting
filename "Code/Pose2SLAMExample-graph.m"
lstparams "aboveskip=10pt,basicstyle={\\ttfamily\\small},captionpos=b,frame=single,identifierstyle={\\bfseries},language={Matlab},numbers=left,caption={Excerpt from matlab/gtsam\\_examples/Pose2SLAMExample\\_graph.m},label={listing:Pose2SLAMExample-graph}"

\end_inset


\end_layout

\begin_layout Subsection
PoseSLAM in 3D
\end_layout

\begin_layout Standard
PoseSLAM can easily be extended to 3D poses, but some care is needed to
 update 3D rotations.
 GTSAM supports both 
\series bold
quaternions
\series default
 and 
\begin_inset Formula $3\times3$
\end_inset

 
\series bold
rotation matrices 
\series default
to represent 3D rotations.
 The selection is made via the compile flag GTSAM_USE_QUATERNIONS.
\end_layout

\begin_layout Standard
\begin_inset Float figure
placement h
wide false
sideways false
status open

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename images/sphere2500-result.pdf
	width 70text%
	BoundingBox 60bp 150bp 610bp 610bp
	clip

\end_inset


\begin_inset Caption Standard

\begin_layout Plain Layout
\begin_inset CommandInset label
LatexCommand label
name "fig:w100-1"

\end_inset

3D plot of sphere example (due to Michael Kaess).
 The very wrong initial estimate, derived from odometry, is shown in green.
 The optimized trajectory is shown red.
 Code below:
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Standard
\begin_inset CommandInset include
LatexCommand lstinputlisting
filename "Code/Pose3SLAMExample-graph.m"
lstparams "aboveskip=10pt,basicstyle={\\ttfamily\\small},captionpos=b,frame=single,identifierstyle={\\bfseries},language={Matlab},numbers=left,caption={Excerpt from matlab/gtsam\\_examples/Pose3SLAMExample\\_graph.m},label={listing:Pose3SLAMExample-graph-1}"

\end_inset


\end_layout

\begin_layout Section
\begin_inset CommandInset label
LatexCommand label
name "sec:Landmark-based-SLAM"

\end_inset

Landmark-based SLAM
\end_layout

\begin_layout Subsection
Basics
\end_layout

\begin_layout Standard
\begin_inset Float figure
placement h
wide false
sideways false
status open

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename images/FactorGraph4.pdf
	scale 80
	BoundingBox 50bp 590bp 290bp 710bp
	clip

\end_inset


\begin_inset Caption Standard

\begin_layout Plain Layout
\begin_inset CommandInset label
LatexCommand label
name "fig:SLAM"

\end_inset

Factor graph for landmark-based SLAM
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Standard
\begin_inset ERT
status open

\begin_layout Plain Layout


\backslash
noindent
\end_layout

\end_inset

 In 
\series bold
landmark-based SLAM
\series default
, we explicitly build a map with the location of observed landmarks, which
 introduces a second type of variable in the factor graph besides robot
 poses.
 An example factor graph for a landmark-based SLAM example is shown in Figure
 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:SLAM"

\end_inset

, which shows the typical connectivity: poses are connected in an odometry
 Markov chain, and landmarks are observed from multiple poses, inducing
 binary factors.
 In addition, the pose 
\begin_inset Formula $x_{1}$
\end_inset

 has the usual prior on it.
\end_layout

\begin_layout Standard
\begin_inset Float figure
placement h
wide false
sideways false
status open

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename images/example2.pdf
	scale 47
	BoundingBox 90bp 220bp 520bp 555bp
	clip

\end_inset


\begin_inset Caption Standard

\begin_layout Plain Layout
\begin_inset CommandInset label
LatexCommand label
name "fig:PlanarSLAMExample"

\end_inset

The optimized result along with covariance ellipses for both poses (in green)
 and landmarks (in blue).
 Also shown are the trajectory (red) and landmark sightings (cyan).
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Standard
The factor graph from Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:SLAM"

\end_inset

 can be created using the MATLAB code in Listing 
\begin_inset CommandInset ref
LatexCommand ref
reference "listing:PlanarSLAMExample"

\end_inset

.
 As before, on line 2 we create the factor graph, and Lines 8-18 create
 the prior/odometry chain we are now familiar with.
 However, the code on lines 20-25 is new: it creates three 
\series bold
measurement factors
\series default
, in this case 
\begin_inset Quotes eld
\end_inset

bearing/range
\begin_inset Quotes erd
\end_inset

 measurements from the pose to the landmark.
\end_layout

\begin_layout Standard
\begin_inset Newpage pagebreak
\end_inset


\end_layout

\begin_layout Standard
\begin_inset CommandInset include
LatexCommand lstinputlisting
filename "Code/PlanarSLAMExample.m"
lstparams "aboveskip=10pt,basicstyle={\\ttfamily\\small},captionpos=b,frame=single,identifierstyle={\\bfseries},language={Matlab},numbers=left,caption={Excerpt from matlab/gtsam\\_examples/PlanarSLAMExample.m},label={listing:PlanarSLAMExample}"

\end_inset


\end_layout

\begin_layout Subsection
Of Keys and Symbols
\end_layout

\begin_layout Standard
\begin_inset ERT
status open

\begin_layout Plain Layout


\backslash
noindent
\end_layout

\end_inset

 The only unexplained code is on lines 4-6: here we create integer keys
 for the poses and landmarks using the 
\series bold
\emph on
symbol
\series default
\emph default
 function.
 In GTSAM, we address all variables using the 
\series bold
\emph on
Ke
\emph default
y
\series default
 type, which is just a typedef to 
\series bold
\emph on
size_t
\series default
\emph default

\begin_inset Foot
status open

\begin_layout Plain Layout
a 32 or 64 bit integer, depending on your platform
\end_layout

\end_inset

.
 The keys do not have to be numbered continuously, but they do have to be
 unique within a given factor graph.
 For factor graphs with different types of variables, we provide the 
\series bold
\emph on
symbol
\series default
\emph default
 function in MATLAB, and the 
\series bold
\emph on
Symbol
\series default
\emph default
 type in C++, to help you create (large) integer keys that are far apart
 in the space of possible keys, so you don't have to think about starting
 the point numbering at some arbitrary offset.
 To create a a 
\emph on
symbol key
\emph default
 you simply provide a character and an integer index.
 You can use base 0 or 1, or use arbitrary indices: it does not matter.
 In the code above, we we use 'x' for poses, and 'l' for landmarks.
\begin_inset Note Note
status collapsed

\begin_layout Plain Layout
, and use the resulting keys 
\series bold
\emph on
i1
\series default
\emph default
, 
\series bold
\emph on
i2
\series default
\emph default
, 
\series bold
\emph on
i3
\series default
\emph default
, 
\series bold
\emph on
j1
\series default
\emph default
, and 
\series bold
\emph on
j2
\series default
\emph default
 to create the factors in the correct way.
\end_layout

\end_inset


\end_layout

\begin_layout Standard
The optimized result for the factor graph created by Listing 
\begin_inset CommandInset ref
LatexCommand ref
reference "listing:PlanarSLAMExample"

\end_inset

 is shown in Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:PlanarSLAMExample"

\end_inset

, and it is readily apparent that the landmark 
\begin_inset Formula $l_{1}$
\end_inset

 with two measurements is better localized.
 In MATLAB we can also examine the actual numerical values, and doing so
 reveals some more GTSAM magic:
\end_layout

\begin_layout Standard

\size small
\begin_inset CommandInset include
LatexCommand verbatiminput
filename "Code/PlanarSLAMExample.txt"

\end_inset


\end_layout

\begin_layout Standard
\begin_inset ERT
status open

\begin_layout Plain Layout


\backslash
noindent
\end_layout

\end_inset

 Indeed, the keys generated by symbol are automatically detected by the
 
\series bold
\emph on
print
\series default
\emph default
 method in the 
\series bold
\emph on
Values
\series default
\emph default
 class, and rendered in human-readable form 
\begin_inset Quotes eld
\end_inset

x1
\begin_inset Quotes erd
\end_inset

, 
\begin_inset Quotes eld
\end_inset

l2
\begin_inset Quotes erd
\end_inset

, etc, rather than as large, unwieldy integers.
 This magic extends to most factors and other classes where the 
\series bold
Key
\series default
 type is used.
\end_layout

\begin_layout Subsection
A Larger Example
\end_layout

\begin_layout Standard
\begin_inset Float figure
placement h
wide false
sideways false
status open

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename images/littleRobot.pdf
	width 90text%
	BoundingBox 0bp 200bp 612bp 600bp
	clip

\end_inset


\begin_inset Caption Standard

\begin_layout Plain Layout
\begin_inset CommandInset label
LatexCommand label
name "fig:littleRobot"

\end_inset

A larger example with about 100 poses and 30 or so landmarks, as produced
 by gtsam_examples/PlanarSLAMExample_graph.m
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Standard
GTSAM comes with a slightly larger example that is read from a .graph file
 by PlanarSLAMExample_graph.m, shown in Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:littleRobot"

\end_inset

.
 To not clutter the figure only the marginals are shown, not the lines of
 sight.
 This example, with 119 (multivariate) variables and 517 factors optimizes
 in less than 10 ms.
\end_layout

\begin_layout Subsection
A Real-World Example
\end_layout

\begin_layout Standard
\begin_inset Float figure
placement h
wide false
sideways false
status open

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename images/Victoria.pdf
	width 90text%
	BoundingBox 0bp 0bp 420bp 180bp
	clip

\end_inset


\begin_inset Caption Standard

\begin_layout Plain Layout
\begin_inset CommandInset label
LatexCommand label
name "fig:Victoria-1"

\end_inset

Small section of optimized trajectory and landmarks (trees detected in a
 laser range finder scan) from data recorded in Sydney's Victoria Park (dataset
 due to Jose Guivant, U.
 Sydney).
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Standard
A real-world example is shown in Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:Victoria-1"

\end_inset

, using data from a well known dataset collected in Sydney's Victoria Park,
 using a truck equipped with a laser range-finder.
 The covariance matrices in this figure were computed very efficiently,
 as explained in detail in 
\begin_inset CommandInset citation
LatexCommand citep
key "Kaess09ras"

\end_inset

.
 The exact covariances (blue, smaller ellipses) obtained by our fast algorithm
 coincide with the exact covariances based on full inversion (orange, mostly
 hidden by blue).
 The much larger conservative covariance estimates (green, large ellipses)
 were based on our earlier work in 
\begin_inset CommandInset citation
LatexCommand citep
key "Kaess08tro"

\end_inset

.
\end_layout

\begin_layout Standard
\begin_inset Newpage pagebreak
\end_inset


\end_layout

\begin_layout Section
Structure from Motion
\end_layout

\begin_layout Standard
\begin_inset Float figure
placement h
wide false
sideways false
status open

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename images/cube.pdf
	width 80text%
	BoundingBox 60bp 90bp 612bp 380bp
	clip

\end_inset


\begin_inset Caption Standard

\begin_layout Plain Layout
\begin_inset CommandInset label
LatexCommand label
name "fig:SFMExample"

\end_inset

An optimized 
\begin_inset Quotes eld
\end_inset

Structure from Motion
\begin_inset Quotes erd
\end_inset

 with 10 cameras arranged in a circle, observing the 8 vertices of a 
\begin_inset Formula $20\times20\times20$
\end_inset

 cube centered around the origin.
 The camera is rendered with color-coded axes, (RGB for XYZ) and the viewing
 direction is is along the positive Z-axis.
 Also shown are the 3D error covariance ellipses for both cameras and points.
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Standard
\begin_inset ERT
status open

\begin_layout Plain Layout


\backslash
noindent
\end_layout

\end_inset


\series bold
 Structure from Motion
\series default
 (SFM) is a technique to recover a 3D reconstruction of the environment
 from corresponding visual features in a collection of 
\emph on
unordered
\emph default
 images, see Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:SFMExample"

\end_inset

.
 In GTSAM this is done using exactly the same factor graph framework, simply
 using SFM-specific measurement factors.
 In particular, there is a 
\series bold
projection factor
\series default
 that calculates the reprojection error 
\begin_inset Formula $f(x_{i},p_{j};z_{ij},K)$
\end_inset

 for a given camera pose 
\begin_inset Formula $x_{i}$
\end_inset

 (a 
\series bold
\emph on
Pose3
\series default
\emph default
) and point 
\begin_inset Formula $p_{j}$
\end_inset

 (a 
\series bold
\emph on
Point3
\series default
\emph default
).
 The factor is parameterized by the 2D measurement 
\begin_inset Formula $z_{ij}$
\end_inset

 (a 
\series bold
\emph on
Point2
\series default
\emph default
), and known calibration parameters 
\begin_inset Formula $K$
\end_inset

 (of type 
\series bold
\emph on
Cal3_S2
\series default
\emph default
).
 The following listing shows how to create the factor graph: 
\begin_inset CommandInset include
LatexCommand lstinputlisting
filename "Code/SFMExample.m"
lstparams "aboveskip=10pt,basicstyle={\\ttfamily\\small},captionpos=b,frame=single,identifierstyle={\\bfseries},language={Matlab},numbers=left,caption={Excerpt from matlab/gtsam\\_examples/SFMExample.m},label={listing:SFMExample}"

\end_inset


\end_layout

\begin_layout Standard
\begin_inset ERT
status open

\begin_layout Plain Layout


\backslash
noindent
\end_layout

\end_inset

 In Listing 
\begin_inset CommandInset ref
LatexCommand ref
reference "listing:SFMExample"

\end_inset

, assuming that the factor graph was already created, we add measurement
 factors in the double loop.
 We loop over images with index 
\begin_inset Formula $i$
\end_inset

, and in this example the data is given as two cell arrays: Z{i} specifies
 a set of measurements 
\begin_inset Formula $z_{k}$
\end_inset

 in image 
\begin_inset Formula $i$
\end_inset

, and 
\begin_inset Formula $J\{i\}$
\end_inset

 specifies the corresponding point index.
 The specific factor type we use is a 
\series bold
\emph on
GenericProjectionFactorCal3_S2
\series default
\emph default
, which is the MATLAB equivalent of the C++ class 
\series bold
\emph on
GenericProjectionFactor<Cal3_S2>
\series default
\emph default
, where 
\series bold
\emph on
Cal3_S2
\series default
\emph default
 is the camera calibration type we choose to use (the standard, no-radial
 distortion, 5 parameter calibration matrix).
 As before landmark-based SLAM (Section 
\begin_inset CommandInset ref
LatexCommand ref
reference "sec:Landmark-based-SLAM"

\end_inset

), here we use symbol keys except we now use the character 'p' to denote
 points, rather than 'l' for landmark.
\end_layout

\begin_layout Standard
Important note: a very tricky and difficult part of making SFM work is (a)
 data association, and (b) initialization.
 GTSAM does neither of these things for you: it simply provides the 
\begin_inset Quotes eld
\end_inset

bundle adjustment
\begin_inset Quotes erd
\end_inset

 optimization.
 In the example, we simply assume the data association is known (it is encoded
 in the J sets), and we initialize with the ground truth, as the intent
 of the example is simply to show you how to set up the optimization problem.
\end_layout

\begin_layout Section
iSAM: Incremental Smoothing and Mapping
\end_layout

\begin_layout Standard
GTSAM provides an incremental inference algorithm based on a more advanced
 graphical model, the Bayes tree, which is kept up to date by the 
\series bold
iSAM
\series default
 algorithm (incremental Smoothing and Mapping, see 
\begin_inset CommandInset citation
LatexCommand citet
key "Kaess08tro,Kaess12ijrr"

\end_inset

 for an in-depth treatment).
 For mobile robots operating in real-time it is important to have access
 to an updated map as soon as new sensor measurements come in.
 iSAM keeps the map up-to-date in an efficient manner.
 
\end_layout

\begin_layout Standard
Listing 
\begin_inset CommandInset ref
LatexCommand ref
reference "listing:iSAMExample"

\end_inset

 shows how to use iSAM in a simple visual SLAM example.
 In line 1-2 we create a 
\series bold
\emph on
NonlinearISAM
\series default
\emph default
 object which will relinearize and reorder the variables every 3 steps.
 The corect value for this parameter depends on how non-linear your problem
 is and how close you want to be to gold-standard solution at every step.
 In iSAM 2.0, this parameter is not needed, as iSAM2 automatically determines
 when linearization is needed and for which variables.
\end_layout

\begin_layout Standard
The example involves eight 3D points that are seen from eight successive
 camera poses.
 Hence in the first step -which is omitted here- all eight landmarks and
 the first pose are properly initialized.
 In the code this is done by perturbing the known ground truth, but in a
 real application great care is needed to properly initialize poses and
 landmarks, especially in a monocular sequence.
\end_layout

\begin_layout Standard
\begin_inset Newpage pagebreak
\end_inset


\begin_inset CommandInset include
LatexCommand lstinputlisting
filename "Code/VisualISAMExample.cpp"
lstparams "aboveskip=10pt,basicstyle={\\ttfamily\\small},captionpos=b,frame=single,identifierstyle={\\bfseries},language={C++},numbers=left,caption={Excerpt from examples/VisualISAMExample.cpp},label={listing:iSAMExample}"

\end_inset


\end_layout

\begin_layout Standard
The remainder of the code illustrates a typical iSAM loop:
\end_layout

\begin_layout Enumerate
Create factors for new measurements.
 Here, in lines 9-18, a small 
\series bold
\emph on
NonlinearFactorGraph
\series default
\emph default
 is created to hold the new factors of type 
\series bold
\emph on
GenericProjectionFactor<Pose3, Point3, Cal3_S2>
\series default
\emph default
.
\end_layout

\begin_layout Enumerate
Create an initial estimate for all newly introduced variables.
 In this small example, all landmarks have been observed in frame 1 and
 hence the only new variable that needs to be initialized at each time step
 is the new pose.
 This is done in lines 20-22.
 Note we assume a good initial estimate is available as 
\emph on
initial_x[i]
\emph default
.
\end_layout

\begin_layout Enumerate
Finally, we call 
\emph on
isam.update()
\emph default
, which takes the factors and initial estimates, and incrementally updates
 the solution, which is available through the method 
\emph on
isam.estimate()
\emph default
, if desired.
\end_layout

\begin_layout Standard
\begin_inset Newpage pagebreak
\end_inset


\end_layout

\begin_layout Section
More Applications
\end_layout

\begin_layout Standard
While a detailed discussion of all the things you can do with GTSAM will
 take us too far, below is a small survey of what you can expect to do,
 and which we did using GTSAM.
\end_layout

\begin_layout Standard
\begin_inset Note Note
status open

\begin_layout Plain Layout
\begin_inset Float figure
wide false
sideways false
status collapsed

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename figures/SAM/FactorGraph.pdf
	scale 80
	BoundingBox 40bp 600bp 300bp 792bp
	clip

\end_inset


\begin_inset Caption Standard

\begin_layout Plain Layout
\begin_inset CommandInset label
LatexCommand label
name "fig:Filtering"

\end_inset

Factor graph explanation of Filtering.
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Plain Layout
The factor graph for landmark-based SLAM is shown in Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:Filtering"

\end_inset

.
\end_layout

\end_inset


\end_layout

\begin_layout Subsection
Conjugate Gradient Optimization
\end_layout

\begin_layout Standard
\begin_inset Float figure
placement h
wide false
sideways false
status open

\begin_layout Plain Layout
\align center
\begin_inset Graphics
	filename images/Beijing.pdf
	width 70text%
	BoundingBox 100bp 240bp 500bp 550bp
	clip

\end_inset


\begin_inset Caption Standard

\begin_layout Plain Layout
\begin_inset CommandInset label
LatexCommand label
name "fig:Beijing"

\end_inset

 A map of Beijing, with a spanning tree shown in black, and the remaining
 
\emph on
loop-closing
\emph default
 constraints shown in red.
 A spanning tree can be used as a 
\emph on
preconditioner
\emph default
 by GTSAM.
\end_layout

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Standard
GTSAM also includes efficient preconditioned conjugate gradients (PCG) methods
 for solving large-scale SLAM problems.
 While direct methods, popular in the literature, exhibit quadratic convergence
 and can be quite efficient for sparse problems, they typically require
 a lot of storage and efficient elimination orderings to be found.
 In contrast, iterative optimization methods only require access to the
 gradient and have a small memory footprint, but can suffer from poor convergenc
e.
 Our method, 
\emph on
subgraph preconditioning
\emph default
, explained in detail in 
\begin_inset CommandInset citation
LatexCommand citet
key "Dellaert10iros,Jian11iccv"

\end_inset

, combines the advantages of direct and iterative methods, by identifying
 a sub-problem that can be easily solved using direct methods, and solving
 for the remaining part using PCG.
 The easy sub-problems correspond to a spanning tree, a planar subgraph,
 or any other substructure that can be efficiently solved.
 An example of such a subgraph is shown in Figure 
\begin_inset CommandInset ref
LatexCommand ref
reference "fig:Beijing"

\end_inset

.
\end_layout

\begin_layout Subsection
Visual Odometry
\end_layout

\begin_layout Standard
A gentle introduction to vision-based sensing is 
\series bold
Visual Odometry
\series default
 (abbreviated VO, see e.g.
 
\begin_inset CommandInset citation
LatexCommand citet
key "Nister04cvpr2"

\end_inset

), which provides pose constraints between successive robot poses by tracking
 or associating visual features in successive images taken by a camera mounted
 rigidly on the robot.
 GTSAM includes both C++ and MATLAB example code, as well as VO-specific
 factors to help you on the way.
\end_layout

\begin_layout Subsection
Visual SLAM
\end_layout

\begin_layout Standard

\series bold
Visual SLAM
\series default
 (see e.g., 
\begin_inset CommandInset citation
LatexCommand citet
key "Davison03iccv"

\end_inset

) is a SLAM variant where 3D points are observed by a camera as the camera
 moves through space, either mounted on a robot or moved around by hand.
 GTSAM, and particularly iSAM (see below), can easily be adapted to be used
 as the back-end optimizer in such a scenario.
\end_layout

\begin_layout Subsection
Fixed-lag Smoothing and Filtering 
\end_layout

\begin_layout Standard
GTSAM can easily perform recursive estimation, where only a subset of the
 poses are kept in the factor graph, while the remaining poses are marginalized
 out.
 In all examples above we explicitly optimize for all variables using all
 available measurements, which is called 
\series bold
Smoothing
\series default
 because the trajectory is 
\begin_inset Quotes eld
\end_inset

smoothed
\begin_inset Quotes erd
\end_inset

 out, and this is where GTSAM got its name (GT 
\emph on
Smoothing
\emph default
 and Mapping).
 When instead only the last few poses are kept in the graph, one speaks
 of 
\series bold
Fixed-lag Smoothing
\series default
.
 Finally, when only the single most recent poses is kept, one speaks of
 
\series bold
Filtering
\series default
, and indeed the original formulation of SLAM was filter-based 
\begin_inset CommandInset citation
LatexCommand citep
key "Smith87b"

\end_inset

.
\end_layout

\begin_layout Subsection
Discrete Variables and HMMs
\end_layout

\begin_layout Standard
Finally, factor graphs are not limited to continuous variables: GTSAM can
 also be used to model and solve discrete optimization problems.
 For example, a Hidden Markov Model (HMM) has the same graphical model structure
 as the Robot Localization problem from Section 
\begin_inset CommandInset ref
LatexCommand ref
reference "sec:Robot-Localization"

\end_inset

, except that in an HMM the variables are discrete.
 GTSAM can optimize and perform inference for discrete models.
\end_layout

\begin_layout Section*
Acknowledgements
\end_layout

\begin_layout Standard
GTSAM was made possible by the efforts of many collaborators at Georgia
 Tech and elsewhere, including but not limited to Doru Balcan, Chris Beall,
 Alex Cunningham, Alireza Fathi, Eohan George, Viorela Ila, Yong-Dian Jian,
 Michael Kaess, Kai Ni, Carlos Nieto, Duy-Nguyen Ta, Manohar Paluri, Christian
 Potthast, Richard Roberts, Grant Schindler, and Stephen Williams.
 In addition, Paritosh Mohan helped me with the manual.
 Many thanks all for your hard work!
\end_layout

\begin_layout Standard
\begin_inset Newpage pagebreak
\end_inset


\begin_inset CommandInset bibtex
LatexCommand bibtex
bibfiles "gtsam"
options "apalike"

\end_inset


\end_layout

\end_body
\end_document