YRG-SLAM: a study of YOLOv5s-based visual SLAM method in dynamic environments

Jintao Zhang; Chaoyi Wan

doi:10.1117/12.3044989

9 October 2024 YRG-SLAM: a study of YOLOv5s-based visual SLAM method in dynamic environments

Jintao Zhang, Chaoyi Wan

Proceedings Volume 13288, Fourth International Conference on Computer Graphics, Image, and Virtualization (ICCGIV 2024); 1328804 (2024) https://doi.org/10.1117/12.3044989
Event: Fourth International Conference on Computer Graphics, Image, and Virtualization (ICCGIV 2024), 2024, Chengdu, China

Abstract

Most of the current SLAM methods are for experiments in static environments, but most of the practical application scenarios are in dynamic environments, which increases the fitting error of the dynamic feature points and reduces the accuracy of position estimation. In order to eliminate this error, this paper proposes a YRG-SLAM method based on RealSense D435i camera parameters in dynamic environments. In this method, the YOLOv5s target detection thread is added to the original ORB-SLAM3 algorithm structure, the YOLOv5s target detection algorithm is placed on the GPU, and the dynamic feature point rejection module is added to the tracking thread. After acquiring the image, YOLOv5s target detection algorithm first extracts the target features, recognizes the target region in the dynamic environment, and frames it as a dynamic target. The feature points on the dynamic target frame are directly removed by GPU acceleration, and finally feature matching is performed on all remaining static feature points to estimate their positions and orientations. Validation on the publicly available TUM dataset shows that the proposed algorithm in this experiment reduces the absolute trajectory error root-mean-square error by an average of 82.75% in highly dynamic environments compared to the original ORB-SLAM3 algorithm, which significantly improves the localization accuracy and confirms the feasibility of the measure.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jintao Zhang and Chaoyi Wan "YRG-SLAM: a study of YOLOv5s-based visual SLAM method in dynamic environments", Proc. SPIE 13288, Fourth International Conference on Computer Graphics, Image, and Virtualization (ICCGIV 2024), 1328804 (9 October 2024); https://doi.org/10.1117/12.3044989

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KEYWORDS

Detection and tracking algorithms

Target detection

Visualization

Cameras

Error analysis

Object detection

Environmental sensing

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