common:
    lid_topic:  /points_raw     #"/velodyne_points"
    imu_topic:  /imu_raw         #"/imu/data"
    gnss_topic:  /gps/fix         #"/imu/data"
    time_sync_en: false         # ONLY turn on when external time synchronization is really not possible
#NCLT
#    lid_topic: "/points_raw"
#    imu_topic: "/imu_raw"

#KITTI
#    lid_topic: "/kitti/velo/pointcloud"
#    imu_topic: "/kitti/oxts/imu"

#RS LiDar
#    lid_topic: "/rslidar"
#    imu_topic: "/imu"

    
preprocess:
    lidar_type: 2                # 1 for Livox serials LiDAR, 2 for Velodyne LiDAR, 3 for ouster LiDAR, 
    scan_line: 16
    scan_rate: 10                # only need to be set for velodyne, unit: Hz,
    blind: 1

mapping:
    acc_cov: 3.9939570888238808e-03
    gyr_cov: 1.5636343949698187e-03
    b_acc_cov: 6.4356659353532566e-05
    b_gyr_cov: 3.5640318696367613e-05
    fov_degree:    180
    det_range:     100.0
    extrinsic_est_en:  true      # true: enable the online estimation of IMU-LiDAR extrinsic,
    extrinsic_T: [ 0, 0, 0]
    extrinsic_R: [ -1, 0, 0, 
                   0, 1, 0, 
                   0, 0, -1]

    extrinT_Gnss2Lidar: [ 0, 0, 0]
    extrinR_Gnss2Lidar: [ 1, 0, 0, 
                   0, 1, 0, 
                   0, 0, 1]
publish:
    path_en:  true
    scan_publish_en:  true       # false: close all the point cloud output
    dense_publish_en: true       # false: low down the points number in a global-frame point clouds scan.
    scan_bodyframe_pub_en: true  # true: output the point cloud scans in IMU-body-frame

pcd_save:
    pcd_save_en: true
    interval: -1                 # how many LiDAR frames saved in each pcd file; 
                                 # -1 : all frames will be saved in ONE pcd file, may lead to memory crash when having too much frames.

# voxel filter paprams
odometrySurfLeafSize: 0.4                     # default: 0.4 - outdoor, 0.2 - indoor
mappingCornerLeafSize: 0.2                    # default: 0.2 - outdoor, 0.1 - indoor
mappingSurfLeafSize: 0.4                      # default: 0.4 - outdoor, 0.2 - indoor

# robot motion constraint (in case you are using a 2D robot)
z_tollerance: 1000                            # meters
rotation_tollerance: 1000                     # radians

# CPU Params
numberOfCores: 4                              # number of cores for mapping optimization
mappingProcessInterval: 0.15                  # seconds, regulate mapping frequency

# Surrounding map
surroundingkeyframeAddingDistThreshold: 1.0   # meters, regulate keyframe adding threshold	选取关键帧的距离阈值
surroundingkeyframeAddingAngleThreshold: 0.2  # radians, regulate keyframe adding threshold	角度阈值
surroundingKeyframeDensity: 2.0               # meters, downsample surrounding keyframe poses   no_used
surroundingKeyframeSearchRadius: 50.0         # meters, within n meters scan-to-map optimization (when loop closure disabled) no_used

# Loop closure
loopClosureEnableFlag: true
loopClosureFrequency: 4.0                     # Hz, regulate loop closure constraint add frequency
surroundingKeyframeSize: 50                   # submap size (when loop closure enabled)
historyKeyframeSearchRadius: 20.0            # meters, key frame that is within n meters from current pose will be considerd for loop closure
historyKeyframeSearchTimeDiff: 30.0           # seconds, key frame that is n seconds older will be considered for loop closure
historyKeyframeSearchNum: 25                  # number of hostory key frames will be fused into a submap for loop closure
historyKeyframeFitnessScore: 0.3              # icp threshold, the smaller the better alignment

# GPS Settings
useImuHeadingInitialization: false           # if using GPS data, set to "true"
useGpsElevation: false                      # if GPS elevation is bad, set to "false"
gpsCovThreshold: 2.0                        # m^2, threshold for using GPS data
poseCovThreshold: 0 #25.0                      # m^2, threshold for using GPS data  位姿协方差阈值 from isam2


# Visualization
globalMapVisualizationSearchRadius: 100.0    # meters, global map visualization radius,  iktree submap 的搜索范围
globalMapVisualizationPoseDensity: 10      # meters, global map visualization keyframe density
globalMapVisualizationLeafSize: 1.0           # meters, global map visualization cloud density

# visual iktree_map  
visulize_IkdtreeMap: true

# visual iktree_map  
recontructKdTree: true

# Export settings
savePCD: true                         # https://github.com/TixiaoShan/LIO-SAM/issues/3
savePCDDirectory: "/fast_lio_sam_ws/src/FAST_LIO_SAM/PCD/"        # in your home folder, starts and ends with "/". Warning: the code deletes "LOAM" folder then recreates it. See "mapOptimization" for implementation