Research on dynamic robust visual SLAM method based on deep learning

Xuanhui Zhong; Qian Pu; Hui Chai; Min Guo

doi:10.1117/12.3024697

3 April 2024 Research on dynamic robust visual SLAM method based on deep learning

Xuanhui Zhong, Qian Pu, Hui Chai, Min Guo

Author Affiliations +

Proceedings Volume 13078, Second International Conference on Informatics, Networking, and Computing (ICINC 2023); 130780H (2024) https://doi.org/10.1117/12.3024697
Event: Second International Conference on Informatics, Networking, and Computing (ICINC 2023), 2023, Wuhan, China

Abstract

Visual simultaneous localisation and mapping is a fundamental technology in autonomous mobile robotic systems. The presence of dynamic objects in the environment can lead to incorrect feature matching, and factors such as fluctuating external lighting conditions can introduce instability to the system, thus limiting the practical application of VSLAM. In this paper, a robust VSLAM system for dynamic environments is proposed. Based on ORB-SLAM2, we use YOLO5 to enhance the consistency of the front-end combined with optical flow motion detection to detect dynamic targets in the environment and reject their feature points. Superpoint replaces ORB in the feature extraction process, which further enhances the adaptability of the system to the instability of the external environment. From the experimental results, it can be seen that the improved VSLAM, the real motion trajectory is extremely close to the estimated trajectory, and the error is greatly reduced.

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

Citation Download Citation

Xuanhui Zhong, Qian Pu, Hui Chai, and Min Guo "Research on dynamic robust visual SLAM method based on deep learning", Proc. SPIE 13078, Second International Conference on Informatics, Networking, and Computing (ICINC 2023), 130780H (3 April 2024); https://doi.org/10.1117/12.3024697

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KEYWORDS

Feature extraction

Visualization

Motion detection

Optical flow

Target detection

Cameras

Detection and tracking algorithms

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