A fast and high precision multi-robot environment modeling based on M-BFSI: Bidirectional filtering and scene identification method.

Autor: Liu DM; School of Aerospace Science and Technology, Xidian University, Xi'an 710126, China., Cui JS; School of Aerospace Science and Technology, Xidian University, Xi'an 710126, China., Zhong YJ; Shanghai Electro-Mechanical Engineering Institute, Shanghai 201109, China., Min CW; School of Aerospace Science and Technology, Xidian University, Xi'an 710126, China., Zhang FR; China Academy of Space Technology, Xian Branch, Xi'an 710100, China., Feng DZ; School of Aerospace Science and Technology, Xidian University, Xi'an 710126, China.
Jazyk: angličtina
Zdroj: IScience [iScience] 2024 Apr 10; Vol. 27 (5), pp. 109721. Date of Electronic Publication: 2024 Apr 10 (Print Publication: 2024).
DOI: 10.1016/j.isci.2024.109721
Abstrakt: This article designs and implements a fast and high-precision multi-robot environment modeling method based on bidirectional filtering and scene identification. To solve the problem of feature tracking failure caused by large angle rotation, a bidirectional filtering mechanism is introduced to improve the error-matching elimination algorithm. A global key frame database for multiple robots is proposed based on a pretraining dictionary to convert images into a bag of words vectors. The images captured by different sub-robots are compared with the database for similarity score calculation, so as to realize fast identification and search of similar scenes. The coordinate transformation from local map to global map and the cooperative SLAM exploration of multiple robots is completed by the best matching image and the transformation matrix. The experimental results show that the proposed algorithm can effectively close the predicted trajectory of the sub-robot, thus achieving high-precision collaborative environment modeling.
Competing Interests: There are no conflicts of interest to disclose.
(© 2024 The Authors.)
Databáze: MEDLINE
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