录制室内数据集跑 LIO-SAM
录制室内数据集跑 LIO-SAM
LIO-SAM 仅支持 9 轴 IMU，但是实验室仅有 6 轴 IMU，好在原作者推荐了支持 6 轴 IMU 的版本
https://github.com/YJZLuckyBoy/liorf
作者还是做了非常多的优化的，respect！
下面的博客对于配置文件的修改有非常详尽的介绍，参照修改即可
IMU+激光雷达融合使用LIO-SAM建图学习笔记——详细、长文、多图、全流程
P_IinL.x、P_IinL.y、P_IinL.z 和 q_ItoL.x、q_ItoL.y、q_ItoL.z、q_ItoL.w 取的是最后五次迭代结果的平均值
最终的配置文件如下
liorf:
# Topics
pointCloudTopic: "velodyne_points" # Point cloud data
imuTopic: "zedm/zed_node/imu/data" # IMU data
odomTopic: "odometry/imu" # IMU pre-preintegration odometry, same frequency as IMU
gpsTopic: "gps/fixz" # GPS odometry topic from navsat, see module_navsat.launch file
# Frames
lidarFrame: "base_link"
baselinkFrame: "base_link"
odometryFrame: "odom"
mapFrame: "map"
# 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: 25.0 # m^2, threshold for using GPS data
# Export settings
savePCD: true # https://github.com/TixiaoShan/LIO-SAM/issues/3
savePCDDirectory: "/Documents/LIO-SAM/" # in your home folder, starts and ends with "/". Warning: the code deletes "LOAM" folder then recreates it. See "mapOptimization" for implementation
# Sensor Settings
sensor: velodyne # lidar sensor type, 'velodyne' or 'ouster' or 'livox' or 'robosense'
N_SCAN: 16 # number of lidar channel (i.e., Velodyne/Ouster: 16, 32, 64, 128, Livox Horizon: 6)
Horizon_SCAN: 1800 # lidar horizontal resolution (Velodyne:1800, Ouster:512,1024,2048, Livox Horizon: 4000)
downsampleRate: 1 # default: 1. Downsample your data if too many points(line). i.e., 16 = 64 / 4, 16 = 16 / 1
point_filter_num: 1 # default: 3. Downsample your data if too many points(point). e.g., 16: 1, 32: 5, 64: 8
lidarMinRange: 1.0 # default: 1.0, minimum lidar range to be used
lidarMaxRange: 1000.0 # default: 1000.0, maximum lidar range to be used
# IMU Settings
imuType: 0 # 0: 6-axis 1: 9-axis
imuRate: 200.0
imuAccNoise: 3.0451604105922320e-02
imuGyrNoise: 2.1139892533843861e-03
imuAccBiasN: 7.9660177633820517e-04
imuGyrBiasN: 4.3749578117779117e-06
imuGravity: 9.80511
imuRPYWeight: 0.01
# Extrinsics: T_lb (lidar -> imu)
extrinsicTrans: [0.0264051, -0.00276252, 0.021908333]
extrinsicRot: [0.99992251, 0.00791289, 0.00961021,
-0.00786348, 0.99995573, -0.00516815,
-0.00965068, 0.00509218, 0.99994047]
# This parameter is set only when the 9-axis IMU is used, but it must be a high-precision IMU. e.g. MTI-680
extrinsicRPY: [0, -1, 0,
1, 0, 0,
0, 0, 1]
# voxel filter paprams
mappingSurfLeafSize: 0.2 # default: 0.4 - outdoor, 0.2 - indoor
mappingCornerLeafSize: 0.1
odometrySurfLeafSize: 0.2
# 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: 0.0 # seconds, regulate mapping frequency
# Surrounding map
surroundingkeyframeAddingDistThreshold: 1.0 # meters, regulate keyframe adding threshold
surroundingkeyframeAddingAngleThreshold: 0.2 # radians, regulate keyframe adding threshold
surroundingKeyframeDensity: 2.0 # meters, downsample surrounding keyframe poses
surroundingKeyframeSearchRadius: 50.0 # meters, within n meters scan-to-map optimization (when loop closure disabled)
surroundingKeyframeMapLeafSize: 0.5 # downsample local map point cloud
# Loop closure
loopClosureEnableFlag: true
loopClosureFrequency: 1.0 # Hz, regulate loop closure constraint add frequency
surroundingKeyframeSize: 50 # submap size (when loop closure enabled)
historyKeyframeSearchRadius: 15.0 # 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
loopClosureICPSurfLeafSize: 0.5 # downsample icp point cloud
historyKeyframeFitnessScore: 0.3 # icp threshold, the smaller the better alignment
# Visualization
globalMapVisualizationSearchRadius: 1000.0 # meters, global map visualization radius
globalMapVisualizationPoseDensity: 10.0 # meters, global map visualization keyframe density
globalMapVisualizationLeafSize: 1.0 # meters, global map visualization cloud density
# Navsat (convert GPS coordinates to Cartesian)
navsat:
frequency: 50
wait_for_datum: false
delay: 0.0
magnetic_declination_radians: 0
yaw_offset: 0
zero_altitude: true
broadcast_utm_transform: false
broadcast_utm_transform_as_parent_frame: false
publish_filtered_gps: false
# EKF for Navsat
ekf_gps:
publish_tf: false
map_frame: map
odom_frame: odom
base_link_frame: base_link
world_frame: odom
frequency: 50
two_d_mode: false
sensor_timeout: 0.01
# -------------------------------------
# External IMU:
# -------------------------------------
imu0: imu_correct
# make sure the input is aligned with ROS REP105. "imu_correct" is manually transformed by myself. EKF can also transform the data using tf between your imu and base_link
imu0_config: [false, false, false,
true, true, true,
false, false, false,
false, false, true,
true, true, true]
imu0_differential: false
imu0_queue_size: 50
imu0_remove_gravitational_acceleration: true
# -------------------------------------
# Odometry (From Navsat):
# -------------------------------------
odom0: odometry/gps
odom0_config: [true, true, true,
false, false, false,
false, false, false,
false, false, false,
false, false, false]
odom0_differential: false
odom0_queue_size: 10
# x y z r p y x_dot y_dot z_dot r_dot p_dot y_dot x_ddot y_ddot z_ddot
process_noise_covariance: [ 1.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 1.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 10.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0.03, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0.03, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0.25, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0.25, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0.04, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0.01, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.01, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.5, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.01, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.01, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.015]
数据包录制好、配置文件修改好以后，就可以直接进行建图
在启动文件中取消对 GPS 模块的使用，如下
<launch>
<arg name="project" default="liorf"/>
<!-- Parameters -->
<rosparam file="$(find liorf)/config/redwallBot.yaml" command="load" />
<!--- LOAM -->
<include file="$(find liorf)/launch/include/module_loam.launch" />
<!--- Robot State TF -->
<include file="$(find liorf)/launch/include/module_robot_state_publisher.launch" />
<!--- Run Navsat -->
<!-- <include file="$(find liorf)/launch/include/module_navsat.launch" /> -->
<!--- Run Rviz-->
<include file="$(find liorf)/launch/include/module_rviz.launch" />
</launch>
建图效果还可以，估计的轨迹也与实际的轨迹很吻合
