Real-time visual SLAM optimization method based on YOLOv8 and geometric constraints in dynamic scenes

Bingxin Liu; Mingyue Cheng

doi:10.1117/12.3029620

21 June 2024 Real-time visual SLAM optimization method based on YOLOv8 and geometric constraints in dynamic scenes

Bingxin Liu, Mingyue Cheng

Proceedings Volume 13167, International Conference on Remote Sensing, Mapping, and Image Processing (RSMIP 2024); 1316736 (2024) https://doi.org/10.1117/12.3029620
Event: International Conference on Remote Sensing, Mapping and Image Processing (RSMIP 2024), 2024, Xiamen, China

Abstract

To address the issues of robustness and localization accuracy in traditional feature-based simultaneous localization and mapping (SLAM) systems in dynamic environments, we propose a real-time visual SLAM optimization method tailored for dynamic indoor scenarios. This paper builds on the ORB-SLAM2 algorithm framework and introduces a dynamic feature point pruning strategy. The server uses the YOLOv8 object detection algorithm to detect objects in the surrounding images and extracts object information such as bounding boxes and categories. The SLAM client continuously analyzes these results and removes feature points belonging to dynamic objects based on the detected prior information. It also eliminates potential dynamic feature points through epipolar geometry constraints. In cases where a small subset of feature points still satisfy the epipolar constraints but exhibit dynamic behavior, we compute the depth information difference between the current frame and the reference frame. If this difference exceeds a predefined threshold, the feature point is classified as dynamic. Finally, experiments conducted on the TUM dataset demonstrate that our algorithm effectively improves the localization accuracy and robustness of visual SLAM systems in highly dynamic datasets compared to the original ORB-SLAM2 algorithm, resulting in a remarkable 98.01% improvement in the absolute RMSE of the trajectory error.

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

Citation Download Citation

Bingxin Liu and Mingyue Cheng "Real-time visual SLAM optimization method based on YOLOv8 and geometric constraints in dynamic scenes", Proc. SPIE 13167, International Conference on Remote Sensing, Mapping, and Image Processing (RSMIP 2024), 1316736 (21 June 2024); https://doi.org/10.1117/12.3029620

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KEYWORDS

Object detection

Visualization

Detection and tracking algorithms

Pose estimation

Associative arrays

Error analysis

Feature extraction

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