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Dynamic simultaneous localization and mapping based on object tracking in occluded environment

Published online by Cambridge University Press:  27 May 2024

Weili Ding Open the ORCID record for Weili Ding [Opens in a new window] ,
Ziqi Pei ,
Tao Yang  and
Taiyu Chen
Weili Ding*
Affiliation:
School of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei, China
Ziqi Pei
Affiliation:
School of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei, China
Tao Yang
Affiliation:
School of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei, China
Taiyu Chen
Affiliation:
School of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei, China
*
Corresponding author: Weili Ding; Email: weiye51@ysu.edu.cn
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Abstract

In practical applications, many robots equipped with embedded devices have limited computing capabilities. These limitations often hinder the performance of existing dynamic SLAM algorithms, especially when faced with occlusions or processor constraints. Such challenges lead to subpar positioning accuracy and efficiency. This paper introduces a novel lightweight dynamic SLAM algorithm designed primarily to mitigate the interference caused by moving object occlusions. Our proposed approach combines a deep learning object detection algorithm with a Kalman filter. This combination offers prior information about dynamic objects for each SLAM algorithm frame. Leveraging geometric techniques like RANSAC and the epipolar constraint, our method filters out dynamic feature points, focuses on static feature points for pose determination, and enhances the SLAM algorithm’s robustness in dynamic environments. We conducted experimental validations on the TUM public dataset, which demonstrated that our approach elevates positioning accuracy by approximately 54% and boosts the running speed by 75.47% in dynamic scenes.

Keywords

dynamic SLAMstereo visionengineering application scenariosobject detectionKalman filter

Information

Type
Research Article
Information
Robotica , Volume 42 , Issue 7 , July 2024 , pp. 2209 - 2225
Copyright
© The Author(s), 2024. Published by Cambridge University Press

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