This is Team 11's final project git repository for NAVARCH/EECS 568, ROB 530: Mobile Robotics. The title of our project is Evaluation of LeGO-LOAM: Lightweight and Ground-Optimized Lidar Odometry and Mapping. Team members include: Bowen Mu, Chien Erh Lin, Haochen Wu, and Weilin Xu.
LeGO-LOAM, a lightweight and ground-optimized lidar odometry and mapping method, is able to do six degree-of-freedom pose estimation for real time with ground vehicles. In this project we evaluate the LeGO-LOAM which reduced computational expense while keeping similar accuracy compared to LOAM method using codes provided by RobustFieldAutonomyLab/LeGO-LOAM and laboshinl's version of LOAM. The data used for evaluation is raw data (with Velodyne LiDAR data and IMU) from KITTI Odometry Benchmark Dataset, UTBM Dataset and KAIST Urban Dataset. We compared the mapping and odometry results of these data, and analysis the relative motion and mapping error in this report.
The evaluation result on KITTI data set illustrates that position estimation error for odometry is acceptable (around 10 m) and that mapping result can provide concise but adequate information of the environment. By comparing with LOAM, LeGO-LOAM provides better accuracy on mapping and odometry tracking (both rotational and translational), and more efficient since filtering the unreliable point cloud. It is also observed that the loop closure reduces the error in odometry for LeGO-LOAM, and LOAM does not have loop closure ability. However, when the drift is large within a short time (such as sharp turn), the performance of LeGO-LOAM is negatively affected. In addition, from the test on UTBM, LeGO-LOAM also has a limited mapping and tracking ability for the roundabout trajectory.
We recommend you read through the original LOAM paper by Ji Zhang and Sanjiv Singh and the original LeGO-LOAM paper by Tixiao Shan and Brendan Englot. These will give you theoretical understanding of the LOAM and LeGO-LOAM algorithm.
The LeGO-LOAM repository is forked from RobustFieldAutonomyLab/LeGO-LOAM. We changed it to be able to run kitti dataset.
- First, install ROS. We used ROS-kinetic on Ubuntu 16.04. Please follow the instruction of ROS installation page.
- gtsam. GTSAM is a library of C++ classes that implement smoothing and mapping (SAM). You can install it by following code.
git clone https://bitbucket.org/gtborg/gtsam.git
cd gtsam/
mkdir build
cd build
cmake ..
make check
make install
in /usr/local/lib/cmake/GTSAM/GTSAMConfig.cmake, change :17 find_dependency to find_package
To install and compile the code, please clone this respository in src/ folder under catkin workspace.
cd ~/catkin_ws/src/
git https://github.com/chienerh/LeGO-LOAM.git
cd ..
catkin_make -j1
For the first time compiling, add -j1 after catkin_make.
source ./devel/setup.bash
roslaunch lego_loam run.launch > PATH_TO_TXT_FILE.txt
rosbag play PATH_TO_BAG_FILE.bag --clock --topic /kitti/velo/pointcloud /kitti/oxts/imu
- Kitti ran with DHL-64E, so change parameters in utility.h
extern const int N_SCAN = 64;
extern const int Horizon_SCAN = 1800;
extern const float ang_res_x = 0.2;
extern const float ang_res_y = 0.427;
extern const float ang_bottom = 24.9;
extern const int groundScanInd = 50;
- In
featureAssociation.cppline 849 TransformToStart(),float s = 1; - In
featureAssociation.cppline 1752 and 1758, disable TransformToEnd() - Sample ros bag files from this link and put it in your own
PATH_TO_BAG_FILEfolder. - Currently for our kitti rosbag, lidar topic name is
/kitti/velo/pointcloud, and the imu topic name is/kitti/oxts/imu. Change topic names from these two to the topic name in your the rosbag if these are not the correct topic names inrun.launchfile line 16 and the rosbag play command.
- According to this repository
- rosbag can be downloaded on their website
roslaunch lego_loam lego_loam_utbm.launch bag:=PATH_TO_BAG
The loam_velodyne repository is forked from laboshinl's version of LOAM. We changed it to be able to run kitti dataset.
cd ~/catkin_ws/src/
git clone https://github.com/chienerh/loam_velodyne
cd ~/catkin_ws
catkin_make -DCMAKE_BUILD_TYPE=Release
source ~/catkin_ws/devel/setup.bash
roslaunch loam_velodyne loam_velodyne.launch
rosbag play PATH_TO_BAG_FILE.bag
- Rostopic of kitti point cloud is "/kitti/velo/pointcloud", so remap "/multi_scan_points" to "/kitti/velo/pointcloud" in
loam_velodyne.launch. - Set lidar to be "VLP-16" in
loam_velodyne.launch.
- According to this repository
- rosbag can be downloaded on their website
- install GPS driver
roslaunch loam_velodyne loam_velodyne_utbm.launch bag:=PATH_TO_BAG
- Use File Player to publish KAIST data into ros message.
- Rostopic of KAIST dataset point cloud topic is
"/ns1/velodyne_points", so remap"/multi_scan_points"to"/ns1/velodyne_points"inloam_velodyne.launch.
- Download raw sequences synced+rectified data and calibration data from The KITTI Vision Benchmark Suite.
- kitti2bag is used to transform kitti raw data to rosbag.
The sequences that provide ground truth are shown in the below table. The odometry eval kit includes description.
The following table lists the name, start and end frame of each sequence that has been used to extract the visual odometry / SLAM training set
| Seq | Sequence name | Start | End | Category | Size |
|---|---|---|---|---|---|
| 00 | 2011_10_03_drive_0027 | 000000 | 004540 | Residential | 17.6 GB |
| 01 | 2011_10_03_drive_0042 | 000000 | 001100 | Road | 4.5 GB |
| 02 | 2011_10_03_drive_0034 | 000000 | 004660 | Residential | 18.0 GB |
| 03 | 2011_09_26_drive_0067 | 000000 | 000800 | ||
| 04 | 2011_09_30_drive_0016 | 000000 | 000270 | Road | 1.1 GB |
| 05 | 2011_09_30_drive_0018 | 000000 | 002760 | Residential | 10.7 GB |
| 06 | 2011_09_30_drive_0020 | 000000 | 001100 | Residential | 4.3 GB |
| 07 | 2011_09_30_drive_0027 | 000000 | 001100 | Residential | 4.3 GB |
| 08 | 2011_09_30_drive_0028 | 001100 | 005170 | Residential | 20.0 GB |
| 09 | 2011_09_30_drive_0033 | 000000 | 001590 | Residential | 6.2 GB |
| 10 | 2011_09_30_drive_0034 | 000000 | 001200 | Residential | 4.8 GB |
- in launch file, add
<!-- rosbag arg -->
<arg name="ros_bag_name" default="simulation_res"/>
<node pkg="rosbag" type="record" name="record" args="record -O /home/USER_NAME/catkin_ws/src/loam_velodyne/result/bag/$(arg ros_bag_name).bag
/integrated_to_init
/groundtruth_pose/pose"/>/
- to save odometry result, in src/lib/TransformMaintence.cpp line 79, add
printf("%f, %f, %f\n", transformMapped()[3], transformMapped()[4], transformMapped()[5]);
Then run launch file with this command outputing file to a csv
roslaunch loam_velodyne loam_velodyne.launch > PATH_TO_FILE.csv
The odmetry result in saved PATH_TO_TXT_FILE.txt and also in ./src/LeGO-LOAM/result/bag/. This .bag file can transformed to .csv file by:
python ./src/LeGO-LOAM/result/process_rosbag.py
Afterwards, you can use ./src/result_plot.m in the repo to plot and compare PATH_TO_TXT_FILE.txt and the ground truth odemetry txt file. You need to change variables res_file and groundTruthFile in this MATLAB to the correct name of PATH_TO_TXT_FILE.txt and ground truth odemetry txt.