A ros package for robust odometry and mapping using LiDAR with aid of different sensors

Saki-Chen

Last update: Nov 2, 2022

Related tags

Miscellaneous W-LOAM

Overview

W-LOAM

A ros package for robust odometry and mapping using LiDAR with aid of different sensors

Demo Video

https://www.bilibili.com/video/BV1Fy4y1L7kZ?from=search&seid=6228704901331074158&spm_id_from=333.337.0.0

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 18.04

git 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=Release

Data 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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Comments

Some questions

Hi,Chen, Thank your for sharing the great work. The test demo is very good.

Maybe something wrong with the feature extract,the corner features are strange as the figure bellow(the pink is surf and green is corner feature). I use the default parameters.

The first figure is the bag you provide.

And the second figure is my test bag(vlp-16).

opened by chengwei0427 0
报错，这是为什么呢

CMake Error at W-LOAM/src/odometry/CMakeLists.txt:5 (add_executable): Target "imuOdometry_fix_lag" links to target "Boost::timer" but the target was not found. Perhaps a find_package() call is missing for an IMPORTED target, or an ALIAS target is missing?

opened by HIT-Ygq 0

A ros package for robust odometry and mapping using LiDAR with aid of different sensors

Related tags

Overview

W-LOAM

Demo Video

Sensor Configure

Dependency

Build

Data Format

Calibration

Run

Paper

Acknowledgement

You might also like...

Direct LiDAR Odometry: Fast Localization with Dense Point Clouds

Raspberry Pi Pico (RP2040) and Micro-ROS (ROS 2) Integration

The ROS version of ICP Mapping with QPEP Solver (Quadratic Pose Estimation Problems)

This repository uses a ROS node to subscribe to camera (hikvision) and lidar (livox) data. After the node merges the data, it publishes the colored point cloud and displays it in rviz.

RefleXXion is a utility designed to aid in bypassing user-mode hooks utilised by AV/EPP/EDR etc

Robust LiDAR SLAM

Lidar-with-velocity - Lidar with Velocity: Motion Distortion Correction of Point Clouds from Oscillating Scanning Lidars

image_projection is a ROS package to create various projections from multiple calibrated cameras.

Isaac ROS image_pipeline package for hardware-accelerated image processing in ROS2.

Comments

Some questions

报错，这是为什么呢

Owner

Saki-Chen

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