Research on ORB-SLAM Algorithm Based on Windowed Matching Estimation

doi:10.16182/j.issn1004731x.joss.23-0528

Abstract

Abstract:

To address unstability of location accuracy of ORB-SLAM system caused by randomness of camera pose solution method, an improved pose solution method based on feature point windowed matching and analytical ICP is proposed, and the mobile robot ORB-SLAM system is constructed. The extracted feature points are windowed to improve matching efficiency while ensuring good feature point matching, the analytical ICP algorithm is used to solve the camera pose for avoiding iteration, and the windowed pose solution with the smallest error is selected for bundle adjustment to reduce the pose errors caused by local information loss or mismatching. The results show that the proposed method could reduce the trajectory error by more than 30% on average compared with ORB-SLAM2 and ORB-SLAM3, and can decrease the probability of camera tracking failure.

Key words: windowed, feature matching, pose estimation, bundle adjustment, ORB-SLAM, camera trajectory error, mobile robot

CLC Number:

TP391.9

Yao Wanye, Pang Zewei, Sun Peijie, Wang Zhu. Research on ORB-SLAM Algorithm Based on Windowed Matching Estimation[J]. Journal of System Simulation, 2024, 36(9): 2032-2042.

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数据集	算法	最大误差	最小误差	均方根误差
fr1_xyz	ORB-SLAM2	0.077	0.015	0.042
	ORB-SLAM3	0.096	0.023	0.054
	本文算法	0.065	0.006	0.032
fr1_desk	ORB-SLAM2	0.166	0.021	0.049
	ORB-SLAM3	0.346	0.016	0.060
	本文算法	0.119	0.011	0.038
fr1_desk2	ORB-SLAM2	0.131	0.016	0.066
	ORB-SLAM3	0.123	0.028	0.065
	本文算法	0.133	0.010	0.051
fr1_360	ORB-SLAM2	0.805	0.223	0.489
	ORB-SLAM3	0.646	0.235	0.350
	本文算法	0.442	0.122	0.223
office0	ORB-SLAM2	0.080	0.017	0.044
	ORB-SLAM3	0.084	0.021	0.051
	本文算法	0.059	0.010	0.035
office1	ORB-SLAM2	0.127	0.019	0.073
	ORB-SLAM3	0.121	0.025	0.075
	本文算法	0.109	0.012	0.066
living1	ORB-SLAM2	0.205	0.063	0.110
	ORB-SLAM3	0.191	0.065	0.103
	本文算法	0.146	0.033	0.081
living2	ORB-SLAM2	0.042	0.011	0.017
	ORB-SLAM3	0.053	0.016	0.019
	本文算法	0.039	0.010	0.015

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