W-LOAM
A ros package for robust odometry and mapping using LiDAR with aid of different sensors
Demo Video
Sensor Configure
- wheel encoder + steer encoder + LiDAR
This is proposed by this repo - IMU + LiDAR
The imuOdometry is implemented based on
https://github.com/TixiaoShan/LIO-SAM/blob/master/src/imuPreintegration.cpp - configure 1 or 2 + GPS
For mapping a large area, GPS is favored
Dependency
- ROS (tested with melodic)
sudo apt install ros-melodic-gps-common # optional # if you want to visualize with satellite maps, use rviz plugin rviz_satellite cd YOUR_CATKIN_WS/src git clone https://github.com/Saki-Chen/rviz_satellite cd .. && catkin_make -DCMAKE_BUILD_TYPE=Release
- gtsam (tested with 4.0.3)
refer to https://github.com/borglab/gtsam
or for ubuntu 18.04git clone https://github.com/borglab/gtsam cd gtsam git checkout 4.0.3 mkdir build && cd build cmake .. make -j8 sudo make install
Build
cd YOUR_CATKIN_WS/src
git clone https://github.com/Saki-Chen/apa_msgs
git clone https://github.com/Saki-Chen/W-LOAM
cd .. && catkin_make -DCMAKE_BUILD_TYPE=ReleaseData Format
- Point
float32 x
float32 y
float32 z
float32 intensity
// id of the laser scaner
uint16 ring
// time relative to the header stamp
float32 rel_time
- Wheel Counting
std_msgs/Header header
// four counter for four wheel
int64 FL
int64 FR
int64 RL
int64 RR
- Steer Angle
std_msgs/Header header
// positive for clock-wise
float64 angle
- IMU (sensor_msgs/Imu )
Calibration
The extrinsic for sensors is expressed as urdf file in folder launch/config.
Especially, the parameter file for Wheel Odometry is in vehicle_params.
Run
- using provided test bag
1. Download bag
https://pan.baidu.com/s/1v_jl-j4jdTZGjtW3P6c7Eg
password: nk5a
2. run algorithm
// with wheel odometry
roslaunch wloam run.launch simulation:=true
// or with imu odometry
roslaunch wloam run.launch simulation:=true odometry_link:=imu_link
3. run rviz
roslaunch wloam rviz.launch
4. play bag
rosbag play parking-lot.bag --clock
- using dataset provided by lio-sam
goto https://github.com/TixiaoShan/LIO-SAM
1. Find and Download bag
Walking, Park and Garden is tested
2. run algorithm
roslaunch wloam run.launch laser_topic:=points_raw imu_topic:=imu_raw cloud_format:=velodyne robot_name:=lio odometry_link:=imu_link simulation:=true
3. run rviz
roslaunch wloam rviz.launch
4. play bag
rosbag play park.bag --clock
If you want to test GPS, just add option enable_gps:=true when start launch file and check the AerialMapDisplay in rviz.
- using Livox data
goto https://github.com/KIT-ISAS/lili-om
1. Find and Download bag
using KA_Urban_Schloss_1.bag as example
2. run algorithm
roslaunch wloam run.launch laser_topic:=points_raw imu_topic:=imu/data cloud_format:=velodyne robot_name:=livox odometry_link:=imu_link simulation:=true
3. run convertion for livox data
rosrun wloam livox_converter
4. run rviz
roslaunch wloam rviz.launch
5. play bag
rosbag play KA_Urban_Schloss_1.bag --clock imu/data:=nouse gnss:=gps/fix
If you want to test GPS, just add option enable_gps:=true when start launch file and check the AerialMapDisplay in rviz.
Paper
WLOAM Wheel-LiDAR Odometry and Mapping for Autonomous Vehicles
comming soon...
Imu Odometry is adapted from LIO-SAM
@inproceedings{liosam2020shan,
title={LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping},
author={Shan, Tixiao and Englot, Brendan and Meyers, Drew and Wang, Wei and Ratti, Carlo and Rus Daniela},
booktitle={IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
pages={5135-5142},
year={2020},
organization={IEEE}
}
Acknowledgement
This work is inspired by LOAM(Lidar Odometry and Mapping in Real-time) and LIO-SAM(Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping)
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