Visual SLAM Algorithm Based on Feature Point Selection in Dynamic Scenes

doi:10.16182/j.issn1004731x.joss.23-1406

Abstract

Abstract:

To address low positioning accuracy and robustness in traditional visual SLAM algorithms under dynamic conditions, this paper proposes an improved dynamic SLAM algorithm based on feature point selection. Built upon the ORB-SLAM3 framework, it incorporates dynamic region partitioning and feature point filtering.The dynamic region partitioning module utilizes an enhanced RT-DETR object detection algorithm to detect dynamic objects in the images and divides the dynamic regions based on the detection boxes. The feature point selection module utilizes epipolar constraints and optical flow methods to filter out feature points on moving objects, retaining stationary dynamic objects and background points within the detected object bounding boxes for subsequent pose optimization. The improved algorithm strives to retain as many effective feature points as possible for camera pose optimization. Experimental results show that the proposed algorithm improves the RMSE of absolute trajectory error in highly dynamic environments by over 90% on average, while maintaining real-time operation.

Key words: optical flow, epipolar constraint, visual SLAM, dynamic environment, RT-DETR, PP-LCNet

CLC Number:

TP242

Jiang Limei, Chen Xinwei. Visual SLAM Algorithm Based on Feature Point Selection in Dynamic Scenes[J]. Journal of System Simulation, 2025, 37(3): 753-762.

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模型	平均精度/%	平均精度(重叠度50%)/%	参数量/M	每秒浮点运算次数/G	帧率(RTX4060)/(帧/s)
PPLCNet-RT-DETR	45.0	61.9	10	29	137
RT-DETR-R18	46.5	63.8	20	60	100
RT-DETR-R34	48.9	66.8	31	92	72
RT-DETR-R50	53.1	71.3	42	136	49
RT-DETR-R101	54.3	72.7	76	259	30

序列	ORB-SLAM3		本文算法		Improvement
序列	RMSE	STD	RMSE	STD	RMSE	STD
walking_halfsphere	0.262 9	0.091 3	0.019 6	0.009 7	0.925 4	0.893 7
walking_xyz	0.275 0	0.146 7	0.014 9	0.008 0	0.945 8	0.945 4
sitting_static	0.006 6	0.003 2	0.005 6	0.002 6	0.151 5	0.187 5
sitting_halfsphere	0.022 9	0.012 5	0.015 3	0.008 7	0.331 9	0.304 0

序列	ORB-SLAM3		本文算法		Improvement
序列	RMSE	STD	RMSE	STD	RMSE	STD/%
walking_halfsphere	0.153 4	0.098 4	0.013 5	0.007 8	0.911 9	0.920 7
walking_xyz	0.149 7	0.125 7	0.012 1	0.007 2	0.919 1	0.942 7
sitting_static	0.004 8	0.002 4	0.004 6	0.002 3	0.041 6	0.041 6
sitting_halfsphere	0.008 0	0.004 3	0.007 5	0.004 1	0.062 5	0.046 5

序列	DynaSLAM	DS-SLAM	RTD-SLAM	本文算法
序列	RMSE	RMSE	RMSE	RMSE
walking_halfsphere	0.025 4	0.030 3	-	0.019 6
walking_xyz	0.015 3	0.024 7	0.017 6	0.014 9
walking_static	0.006 8	0.008 1	0.006 9	0.006 9
walking_rpy	0.035 6	0.444 2	0.061 2	0.030 4
sitting_xyz	0.009 4	-	0.008 7	0.008 4
sitting_static	-	0.006 5	0.005 8	0.005 6

算法	网络模型	单帧跟踪时间/ms	GPU
ORB-SLAM3	-	16.4	RTX 4060
DynaSLAM	Mask R-CNN	153.9	RTX 4060
本文算法	PPLCNet-RT-DETR	46.8	RTX 4060
R18-RT-DETR-SLAM	RT-DETR-R18	49.5	RTX 4060