A semantic SLAM system for low texture and dynamic environments

Youwei Fang; Qimin Li

doi:10.1117/12.3005821

10 October 2023 A semantic SLAM system for low texture and dynamic environments

Youwei Fang, Qimin Li

Proceedings Volume 12799, Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023); 127992H (2023) https://doi.org/10.1117/12.3005821
Event: 3rd International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023), 2023, Kuala Lumpur, Malaysia

Abstract

Visual Simultaneous Localization and Mapping(vSLAM) is considered the foundation technology for augmented reality and mobile robotics, and a number of impressive systems have been developed over the decades. However, some issues are still not well addressed, such as poor positioning accuracy and even tracking loss in low texture and dynamic environments. We propose a robust semantic visual SLAM for indoor low-texture and dynamic environments. It uses deep learning image feature point extraction method to replace the traditional feature point extraction method and uses case segmentation network to provide image semantic information combined with motion consistency check to eliminate the influence of dynamic objects. Finally, we verify the accuracy and robustness of the proposed system on the TUM RGB-D dataset. The results show that the proposed system has higher accuracy than ORB-SLAM2 and other semantic SLAM.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Youwei Fang and Qimin Li "A semantic SLAM system for low texture and dynamic environments", Proc. SPIE 12799, Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023), 127992H (10 October 2023); https://doi.org/10.1117/12.3005821

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KEYWORDS

Semantics

Feature extraction

Deep learning

Dynamical systems

Visualization

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