基于窗口化匹配估计的ORB-SLAM算法研究

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

摘要/Abstract

摘要：

针对ORB-SLAM系统中求解相机位姿存在随机性而引起定位精度不稳定的问题，提出一种基于特征点窗口化匹配和解析ICP的改进位姿求解算法，构建移动机器人ORB-SLAM系统。通过对提取的特征点进行窗口化，在保证选取良好特征点匹配的同时提高匹配效率；利用具有解析解的ICP算法求解相机位姿避免迭代，选取误差最小的窗口位姿求解结果进行光束法平差优化，以减小局部信息丢失或误匹配引起的位姿误差。结果表明：改进算法能够提高相机轨迹求解精度，轨迹误差相比ORB-SLAM2和ORB-SLAM3平均减少30%以上，并可以降低相机跟踪失败的概率。

关键词: 窗口化, 特征匹配, 位姿估计, 光束法平差, ORB-SLAM, 相机轨迹误差, 移动机器人

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

中图分类号:

TP391.9

姚万业,庞泽伟,孙沛杰等 . 基于窗口化匹配估计的ORB-SLAM算法研究[J]. 系统仿真学报, 2024, 36(9): 2032-2042.

Yao Wanye,Pang Zewei,Sun Peijie,et al . 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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