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Journal of System Simulation

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.

First Page

753

Last Page

762

CLC

TP242

Recommended Citation

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.

Corresponding Author

Chen Xinwei

DOI

10.16182/j.issn1004731x.joss.23-1406

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