ArXiv Preprint (arXiv 2303.10882)
Accepted in CVPR 2023
Refer to: fishmarch repo
The only difference of this repo w.r.t fishmarch repo is using OR-Tools instead of Gurobi to achieve the same result of map compression.
The method is tested with ORB-SLAM2. Please follow their instruction to install prerequisites.
The mix-integer programming is solved using GUROBI.
Google Optimization Tools (a.k.a., OR-Tools) is an open-source, fast and portable software suite for solving combinatorial optimization problems.
If you want to run this repo, please first install OR-Tools on your machine.
The method now is testing with RGBD and stereo mode of ORB-SLAM2, and tested in ICL-NUIM, EuRoC, and KITTI datasets.
Take EuRoC data for example.
Examples/Stereo/stereo_euroc_savemap Vocabulary/ORBvoc.txt Examples/Stereo/EuRoC.yaml PATH_TO_SEQUENCE/mav0/cam0/data PATH_TO_SEQUENCE/mav0/cam1/data Examples/Stereo/EuRoC_TimeStamps/SEQUENCE.txt PATH_TO_MAP
Examples/Tools/slam_compress_map Vocabulary/ORBvoc.txt Examples/Stereo/EuRoC.yaml PATH_TO_MAP PATH_TO_COMPRESSED_MAP COMPRESS_METHPD
COMPRESS_METHOD :
- 1: Compress based only on 2D grids
- 2: Compress based on 2D and 3D grids
Examples/Stereo/stereo_euroc_localize Vocabulary/ORBvoc.txt Examples/Stereo/EuRoC.yaml PATH_TO_SEQUENCE/mav0/cam0/data PATH_TO_SEQUENCE/mav0/cam1/data Examples/Stereo/EuRoC_TimeStamps/SEQUENCE.txt PATH_TO_COMPRESSED_MAP
@InProceedings{Zhang_2023_CVPR,
author = {Zhang, Xiaoyu and Liu, Yun-Hui},
title = {Efficient Map Sparsification Based on 2D and 3D Discretized Grids},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2023},
pages = {12470-12478}
}