Robust Place Recognition using an Imaging Lidar
A place recognition package using high-resolution imaging lidar. For best performance, a lidar equipped with more than 64 uniformly distributed channels is strongly recommended, i.e., Ouster OS1-128 lidar.
Dependency
- ROS
- DBoW3
cd ~/Downloads/ git clone https://github.com/rmsalinas/DBow3.git cd ~/Downloads/DBow3/ mkdir build && cd build cmake -DCMAKE_BUILD_TYPE=Release .. sudo make install
Install Package
Use the following commands to download and compile the package.
cd ~/catkin_ws/src
git clone https://github.com/TixiaoShan/imaging_lidar_place_recognition.git
cd ..
catkin_make
Notes
Download
The three datasets used in the paper can be downloaded from from Google Drive. The lidar used for data-gathering is Ouster OS1-128.
https://drive.google.com/drive/folders/1G1kE8oYGKj7EMdjx7muGucXkt78cfKKU?usp=sharing
Point Cloud Format
The author defined a customized point cloud format, PointOuster, in parameters.h. The customized point cloud is projected onto various images in image_handler.h. If you are using your own dataset, please modify these two files to accommodate data format changes.
Visualization logic
In the current implementation, the package subscribes to a path message that is published by a SLAM framework, i.e., LIO-SAM. When a new point cloud arrives, the package associates the point cloud with the latest pose in the path. If a match is detected between two point clouds, an edge marker is plotted between these two poses. The reason why it's implemented in this way is that SLAM methods usually suffer from drift. If a loop-closure is performed, the associated pose of a point cloud also needs to be updated. Thus, this visualization logic can update point clouds using the updated path rather than using TF or odometry that cannot be updated later.
Image Crop
It's recommended to set the image_crop parameter in params.yaml to be 196-256 when testing the indoor and handheld datasets. This is because the operator is right behind the lidar during the data-gathering process. Using features extracted from the operator body may cause unreliable matching. This parameter should be set to 0 when testing the Jackal dataset, which improves the reverse visiting detection performance.
Test Package
- Run the launch file:
roslaunch imaging_lidar_place_recognition run.launch
- Play existing bag files:
rosbag play indoor_registered.bag -r 3
Paper
Thank you for citing our paper if you use any of this code or datasets.
@inproceedings{robust2021shan,
title={Robust Place Recognition using an Imaging Lidar},
author={Shan, Tixiao and Englot, Brendan and Duarte, Fabio and Ratti, Carlo and Rus Daniela},
booktitle={IEEE International Conference on Robotics and Automation (ICRA)},
pages={to-be-added},
year={2021},
organization={IEEE}
}
3 Oct 15, 2022
70 Dec 22, 2022
43 Nov 21, 2022
25 Sep 26, 2022
49 Oct 24, 2022
![[ICRA 2022] CaTGrasp: Learning Category-Level Task-Relevant Grasping in Clutter from Simulation](https://github.com/wenbowen123/catgrasp/raw/master/media/intro.jpg)
199 Jan 4, 2023
![[ICRA 2022] An opensource framework for cooperative detection. Official implementation for OPV2V.](https://github.com/DerrickXuNu/OpenCOOD/raw/main/images/demo1.gif)
322 Dec 23, 2022
35 Dec 26, 2022
29 Sep 24, 2022
thanks for your answering!
1.3k Jan 8, 2023
37 Oct 9, 2022
136 Jan 3, 2023
20 Feb 3, 2022
232 Dec 25, 2022
70 Jan 2, 2023
47 Jun 30, 2022
31 Dec 8, 2022
276 Dec 30, 2022
4 Jul 27, 2022