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© 2023. This work is published under http://creativecommons.org/licenses/by-nc/4.0/ (the "License"). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

Moving humans, agents, and subjects bring many challenges to robot self‐localisation and environment perception. To adapt to dynamic environments, SLAM researchers typically apply several deep learning image segmentation models to eliminate these moving obstacles. However, these moving obstacle segmentation methods cost too much computation resource for the onboard processing of mobile robots. In the current industrial environment, mobile robot collaboration scenario, the noise of mobile robots could be easily found by on‐board audio‐sensing processors and the direction of sound sources can be effectively acquired by sound source estimation algorithms, but the distance estimation of sound sources is difficult. However, in the field of visual perception, the 3D structure information of the scene is relatively easy to obtain, but the recognition and segmentation of moving objects is more difficult. To address these problems, a novel vision‐audio fusion method that combines sound source localisation methods with a visual SLAM scheme is proposed, thereby eliminating the effect of dynamic obstacles on multi‐agent systems. Several heterogeneous robots experiments in different dynamic scenes indicate very stable self‐localisation and environment reconstruction performance of our method.

Details

Title
Vision‐audio fusion SLAM in dynamic environments
Author
Zhang, Tianwei 1   VIAFID ORCID Logo  ; Zhang, Huayan 2   VIAFID ORCID Logo  ; Li, Xiaofei 3 

 Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen, China 
 Department of Robotics, Ritsumeikan University, Shiga, Japan 
 Westlake University and Westlake Institute for Advanced Study, Hangzhou, China 
Pages
1364-1373
Section
SPECIAL SECTION:INTELLIGENCE TECHNOLOGY FOR REMOTE SENSING IMAGE
Publication year
2023
Publication date
Dec 1, 2023
Publisher
John Wiley & Sons, Inc.
e-ISSN
24682322
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
3091965436
Copyright
© 2023. This work is published under http://creativecommons.org/licenses/by-nc/4.0/ (the "License"). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.