Title: A dynamic visual SLAM method based on ORB-SLAM3 for intelligent mobile robots

Authors: Yong-xun Yu; Jie Yu; Peng-hui Fu; Xiao-lei Yan

Addresses: Department of Intelligent Manufacturing Engineering, Meizhouwan Vocational Technology College, Putian, 351119, China ' Fujian Key Laboratory of Automotive Electronics and Electric Drive, Fujian University of Technology, Fuzhou, 350118, China ' Fujian Key Laboratory of Automotive Electronics and Electric Drive, Fujian University of Technology, Fuzhou, 350118, China ' Fujian Key Laboratory of Automotive Electronics and Electric Drive, Fujian University of Technology, Fuzhou, 350118, China

Abstract: Accurately detecting and removing dynamic targets is crucial for enhancing the precision of visual simultaneous localisation and mapping (SLAM) systems in complex environments. To achieve high-precision and robust visual SLAM in dynamic settings, we propose a novel method called Dynamic-objects Semantic Visual SLAM, which integrates ORB-SLAM3 with YOLOv8. First, YOLOv8 is employed to detect and segment dynamic objects in real-time, and the feature information of these objects is seamlessly integrated into the ORB-SLAM3 front-end. Sparse optical flow tracking is subsequently utilised to track dynamic objects across frames, while enhanced ulti-view geometry addresses potential incomplete object detection issues in semantic segmentation. Finally, highly dynamic objects are filtered out to generate accurate localised maps. The dataset Technische Universität München (TUM) is used for experimental evaluation, and the results show that the absolute pose error is reduced by 78.63% and the relative pose error by 81.38%, significantly improving the success rate of mapping.

Keywords: SLAM; simultaneous localisation and mapping; YOLOv8; instance segmentation; dynamic object detection.

DOI: 10.1504/IJVSMT.2025.150166

International Journal of Vehicle Systems Modelling and Testing, 2025 Vol.19 No.4, pp.353 - 373

Received: 16 Nov 2024
Accepted: 10 Mar 2025
Published online: 02 Dec 2025 *

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