NDT-Map-Code: A 3D global descriptor for real-time loop closure detection in lidar SLAM
You can find the paper here NDT-Map-Code.
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We implemented the SLAM package of NDTMC and LIOSAM integration, which can be found at NDTMC-LIO-SAM.
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We tested our descriptor on KITTI sequences 00, 02, 05, 06, 07 and 08:
- F1 score and extended precision results on the KITTI dataset:
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Create GT of LCD
Use script/create_ground_truth.py to generate the LCD GT value, you need to modify the two file paths in the script/create_ground_truth.py (odometry ground truth file path, LCD GT value result path).
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Save descriptors
Modify the main function in run_demo.cpp as follow:
int main(int argc, char **argv) { saveDesc(argv); // matchForKitti(argv); // matchForNIO(argv); return 0; }
Build and run script/auto_test.py to generate descriptors, where input_folder and output_folder need to be modified to the voledyne folder path of the currently processed sequence and the folder path where you want to save the descriptor results, respectively.
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Descriptors matching
Modify the main function in run_demo.cpp as follow:
int main(int argc, char **argv) { // saveDesc(argv); matchForKitti(argv); // matchForNIO(argv); return 0; }
Build and run script/auto_match.py to perform descriptor matching, where input_folder and output_txt need to be modified to the folder path where the descriptor was saved in the previous step and the txt file path where you want to save the matching result, respectively.
At this point, you can get the matching result of each frame, and you can process it yourself to get more result information, such as: precision-recall curve, F1 Score and Extended Precision.
@misc{liao2023ndtmapcode,
title={NDT-Map-Code: A 3D global descriptor for real-time loop closure detection in lidar SLAM},
author={Lizhou Liao and Li Sun and Xinhui Bai and Zhenxing You and Hongyuan Yuan and Chunyun Fu},
year={2023},
eprint={2307.08221},
archivePrefix={arXiv},
primaryClass={cs.RO}
}
- Maintainer: Lizhou Liao (
[email protected]
)
- Lizhou Liao: completed the code
- Thanks for NIO low-speed localization and mapping group.