RGB-D visual SLAM for point association local edge features

Hongtu Li; Fang Wang; Yunjiang Zhang

doi:10.1117/12.3014358

9 January 2024 RGB-D visual SLAM for point association local edge features

Hongtu Li, Fang Wang, Yunjiang Zhang

Proceedings Volume 12969, International Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023); 129692N (2024) https://doi.org/10.1117/12.3014358
Event: International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023), 2023, Qingdao, China

Abstract

Aiming at the difficulty of point feature matching in 3D reconstruction to meet the tracking requirements of weakly textured scenes, this paper proposes a visual SLAM algorithm based on grid method combining points with edge features. In the tracking thread, a method based on grid method is proposed to evaluate the feature quality of points. The textures of external environment are judged according to ORB feature description, and the information of Canny edge features of weakly textured mesh is added to improve the positioning accuracy. In the local mapping thread, the joint feature points pose and map points are iteratively optimized to improve the convergence rate of the algorithm. The simulation results show that the proposed algorithm has a good location and tracking effects in the weak texture scene.

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

Citation Download Citation

Hongtu Li, Fang Wang, and Yunjiang Zhang "RGB-D visual SLAM for point association local edge features", Proc. SPIE 12969, International Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023), 129692N (9 January 2024); https://doi.org/10.1117/12.3014358

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