3D-SLAM入门教程-多线雷达SC-LIO-SAM三维建图
3D-SLAM入门教程-多线雷达SC-LIO-SAM三维建图
说明：
- 介绍SC-LIO-SAM安装和使用
步骤：
- 安装ros依赖：
sudo apt-get install -y ros-kinetic-navigation
sudo apt-get install -y ros-kinetic-robot-localization
sudo apt-get install -y ros-kinetic-robot-state-publisher
- 安装gstm：
wget -O ~/Downloads/gtsam.zip https://github.com/borglab/gtsam/archive/4.0.2.zip
cd ~/Downloads/ && unzip gtsam.zip -d ~/Downloads/
cd ~/Downloads/gtsam-4.0.2/
- gtsam需要的eigen 需要改成使用系统的eigen，看问题描述
要在 gtsam 的 CMakeLists.txt 中 找到这句话 "if(GTSAM_USE_SYSTEM_EIGEN) find_package(Eigen3 REQUIRED)"
在这句话之前插入 "set(GTSAM_USE_SYSTEM_EIGEN ON)" ，即可。
- 编译
mkdir build && cd build
cmake -DGTSAM_BUILD_WITH_MARCH_NATIVE=OFF ..
sudo make install -j8
- 安装lio-slam
cd ~/catkin_ws/src
git clone https://github.com/gisbi-kim/SC-LIO-SAM
cd ..
catkin_make
数据集测试：
下载数据集，百度网盘，https://pan.baidu.com/s/1-sAB_cNlYPqTjDuaFgz9pg，提取码ejmu
运行walk数据包不需要改params.yaml文件
其他两个数据包运行要修改topics和extrinsicRPY，extrinsicRot。
需要保存pcd请修改保存true和路径。
调大_TIMEOUT_SIGINT值
sudo gedit /opt/ros/kinetic/lib/python2.7/dist-packages/roslaunch/nodeprocess.py
- params.yaml配置修改
lio_sam:
# Topics
pointCloudTopic: "points_raw" # Point cloud data
imuTopic: "imu_correct" # IMU data
odomTopic: "odometry/imu" # IMU pre-preintegration odometry, same frequency as IMU
gpsTopic: "odometry/gpsz" # GPS odometry topic from navsat, see module_navsat.launch file
# 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: "/data/lio/" # in your home folder, starts and ends with "/". Warning: the code deletes "LOAM" folder then recreates it. See "mapOptimization" for implementation
# Sensor Settings
N_SCAN: 16 # number of lidar channel (i.e., 16, 32, 64, 128)
Horizon_SCAN: 1800 # lidar horizontal resolution (Velodyne:1800, Ouster:512,1024,2048)
timeField: "time" # point timestamp field, Velodyne - "time", Ouster - "t"
downsampleRate: 1 # default: 1. Downsample your data if too many points. i.e., 16 = 64 / 4, 16 = 16 / 1
# IMU Settings
imuAccNoise: 3.9939570888238808e-03
imuGyrNoise: 1.5636343949698187e-03
imuAccBiasN: 6.4356659353532566e-05
imuGyrBiasN: 3.5640318696367613e-05
imuGravity: 9.80511
# Extrinsics (lidar -> IMU)
extrinsicTrans: [0.0, 0.0, 0.0]
extrinsicRPY: [1, 0, 0,
0, 1, 0,
0, 0, 1]
extrinsicRot: [1, 0, 0,
0, 1, 0,
0, 0, 1]
# extrinsicRPY: [1, 0, 0,
# 0, 1, 0,
# 0, 0, 1]
# LOAM feature threshold
edgeThreshold: 1.0
surfThreshold: 0.1
edgeFeatureMinValidNum: 10
surfFeatureMinValidNum: 100
# voxel filter paprams
odometrySurfLeafSize: 0.4 # default: 0.4
mappingCornerLeafSize: 0.2 # default: 0.2
mappingSurfLeafSize: 0.4 # default: 0.4
# 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
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)
# Loop closure
loopClosureEnableFlag: false
surroundingKeyframeSize: 25 # 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
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]
- 运行SC-LIO-SAM：
roslaunch lio_sam run.launch
- 运行rosbag
rosbag play your-bag.bag -r 3
参考：
- https://github.com/gisbi-kim/SC-LIO-SAM
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