Article: Enhanced SLAM based on 2D LiDAR and RGB-D camera fusion for mobile robots navigation Journal: International Journal of Vehicle Systems Modelling and Testing (IJVSMT) 2025 Vol.19 No.1 pp.28 - 46 Abstract: Real-time mapping and dynamic navigation for mobile robots present significant challenges, particularly due to the limitations of 2D LiDAR in environmental representation and the constraints of using a single RGB-D camera. This paper introduces a novel mapping method that enhances the traditional ORB-SLAM2 system by integrating 2D LiDAR and RGB-D camera data using Bayesian estimation. This approach enables the construction of dense maps, OctoMaps, and grid maps, improving the completeness and practicality of the mapping process. Additionally, Cartographer-SLAM is incorporated into the enhanced ORB-SLAM2 framework to further refine mapping capabilities. Comparative tests using the publicly available TUM dataset show that the proposed method reduces absolute pose error by 51.16%, with mapping trajectories closely aligning with ground truth values. The camera tracking trajectory improves by 16.2%. Experimental results demonstrate that the novel algorithm provides clearer environmental representations, increased accuracy, and higher mapping success rates. Inderscience Publishers - linking academia, business and industry through research

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Title: Enhanced SLAM based on 2D LiDAR and RGB-D camera fusion for mobile robots navigation

Authors: Jie Yu; Peng-Hui Fu; Qing-Yong Zhang; Xiao-Lei Yan; Sheng Ye

Addresses: 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 ' 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: Real-time mapping and dynamic navigation for mobile robots present significant challenges, particularly due to the limitations of 2D LiDAR in environmental representation and the constraints of using a single RGB-D camera. This paper introduces a novel mapping method that enhances the traditional ORB-SLAM2 system by integrating 2D LiDAR and RGB-D camera data using Bayesian estimation. This approach enables the construction of dense maps, OctoMaps, and grid maps, improving the completeness and practicality of the mapping process. Additionally, Cartographer-SLAM is incorporated into the enhanced ORB-SLAM2 framework to further refine mapping capabilities. Comparative tests using the publicly available TUM dataset show that the proposed method reduces absolute pose error by 51.16%, with mapping trajectories closely aligning with ground truth values. The camera tracking trajectory improves by 16.2%. Experimental results demonstrate that the novel algorithm provides clearer environmental representations, increased accuracy, and higher mapping success rates.

Keywords: SLAM; simultaneous localisation and mapping; RGB-D camera; information fusion; Bayesian estimation.

DOI: 10.1504/IJVSMT.2025.145566

International Journal of Vehicle Systems Modelling and Testing, 2025 Vol.19 No.1, pp.28 - 46

Received: 24 Oct 2024
Accepted: 25 Dec 2024
Published online: 04 Apr 2025 *

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Title: Enhanced SLAM based on 2D LiDAR and RGB-D camera fusion for mobile robots navigation

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