- For more details for each algorithm,
Quatro https://github.com/url-kaist/quatro
LeGO LOAM https://github.com/RobustFieldAutonomyLab/LeGO-LOAM - In Quatro registration example, we used fast point feature histogram (FPFH), Patchwork, and etc. to reduce the computational time of feature extraction & matching, i.e. the front-end of global registration, from tens of seconds to almost 0.2 sec.
- But note that Quatro-LeGO-LOAM only uses the FPFH descriptor to perform feature matching and then estimates a relative pose through Quatro.
- LeGO-LOAM has a large drift, which is accumulated in large-scale maps. For this reason, loop detection using Radius-Search may not generate the loop closure constraint properly. Therefore, if the drift is larger than the used KITTI 05 sequence, it is better to use another descriptor or another Odometry method. Then, You can use
Quatro-SC-LeGO-LOAM or Quatro-Faster-LIOalterlatively. It hasn't been released yet.😭
The code is tested successfully at
- Ubuntu 18.04 LTS + ROS Melodic main branch
- Ubuntu 20.04 LTS + ROS Noetic noetic branch
- First of all, you need to build Quatro (link).
- Run the following script. We use catkin tools,
mkdir -p ~/catkin_ws/src
cd ~/catkin_ws/src
git clone git@github.com:url-kaist/quatro.git
cd quatro && mkdir build && cd build
# To build Quatro, `pmc-src` should be placed in `build` directory in catkin workspace
# i.e. `~/catkin_ws/build/pmc-src`
cmake ..
mv pmc-src/ ../../../build/
cd ~/catkin_ws
catkin build quatro
Note that without pmc-src
, the below error occurs!
CMake Error at quatro/CMakeLists.txt:53 (add_subdirectory):
add_subdirectory given source "~/catkin_ws/build/pmc-src" which
is not an existing directory.
cd ~/catkin_ws/src
git clone {this repo}
cd ..
catkin_make
source devel/setup.bash
roslaunch lego_loam run.launch
- Download KITTI 05 sequence dataset with following command. We already make a rosbag file as an example (15.6GB)
wget https://urserver.kaist.ac.kr/publicdata/quatro/kitti_sequence_05.bag
- You can run the KITTI bag file as follows:
rosbag play kitti_sequence_05.bag --clock --topics /kitti/velo/pointcloud
- To generate more bags using other KITTI raw data, you can use the python script kitti2bag.
- Check the parameters according to the various lidars in utility.h :
VLP-16
HDL-32E
HDL-64E
VLS-128
OS1-16
OS1-64
OS0-128
- You can run your bag file as below code.
rosbag play {your_bag}.bag --topics {pointcloud_topic_message}
- I used evo (link) to evaluate the trajectories.
- You can install evo using the following command
pip install evo --upgrade --no-binary evo
- First, you have to modify the directory
traDirectory
inutility.h
file.
// at 62 line of utility.h
extern const string traDirectory = "/home/{your_dir}/lego_loam_trajectory/";
- The following command can be used to evaluate the trajectory.
evo_traj kitti {your_file_name}.txt --ref={kitti_groundtruth_file_name}.txt -p --plot_mode=xyz
- Also, You can use other methods to evaluate your trajectories e.g. metric of trajectory.
mkdir results
evo_ape kitti [kitti_file_name].txt [your_file_name].txt -va --plot --plot_mode xyz --save_results results/[name].zip
evo_ape kitti [kitti_file_name].txt [your_file_name].txt -va --plot --plot_mode xyz --save_results results/[name]
- You can also save final PCD file. Modify your
pcdDirectory
inutility.h
file.
// at 60 line of utility.h
extern cost string pcdDirectory = "/home/{your_dir}"
- Compare LeGO-LOAM and Quatro-LeGO-LOAM with KITTI 05 dataset. We used cloudcompare to visualize pcl.
- You can download cloudcompare with folloing command.
snap install cloudcompare
- Trajectory is evaluated by evo (link).
-
From the gif below,
red : source
,green : target
,blue : estimation
. -
Even though they are the same Radius Search and ICP parameters, they differ greatly depending on Quatro's Initial Guess.
-
Existing Lego-loam does not satisfy the icp threshold due to the distance between src and tgt, but using ICP after Quatro produces much better ICP results.
- Quatro-Lego-Loam's Trajectory evaluation showed an error of less than 10cm !!
Dataset | scene1 | scene2 |
---|---|---|
KITTI 05 seq. |
- You can visualize Quatro's source, target, estimation clouds :
/quatro_src
,/quatro_tgt
,/quatro_est
. - Below visualization picture is an example.
red : source
,green : target
,blue : estimation