IEMEK Journal of Embedded Systems and Applications (대한임베디드공학회논문지)

Institute of Embedded Engineering of Korea (대한임베디드공학회)
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Adaptive Sampling-Based Particle Filtering for Solving the Rank Deficiency Problem of Robot Localization in Corridor Environments

복도 환경에서 로봇 위치추정의 랭크 결핍 문제 해결을 위한 적응적 샘플링 기반 파티클 필터링 기법

  • Suhyeon Kang (Department of IT Convergence Engineering, Kumoh National Institute of Technology) ;
  • Yujin Kwon (School of Electronic Engineering, Kumoh National Institute of Technology) ;
  • Heoncheol Lee (Department of IT Convergence Engineering, Kumoh National Institute of Technology)
  • 강수현 ;
  • 권유진 ;
  • 이헌철
  • Received : 2024.06.18
  • Accepted : 2024.08.13
  • Published : 2024.08.31
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Abstract

This research addresses the problem of robot localization in corridor environments using LiDAR (Light Detection and Ranging). Due to the rank deficiency problem in scan matching with LiDAR alone, the accuracy of robot localization may degenerate seriously. This paper proposes an adaptive sampling-based particle filtering method using depth sensors to overcome the rank deficiency problem. The increase in the sample size in particle filters can be considered to solve the problem. But, it may cause much computation cost. In the proposed method, the sample size of the particle set in the proposed method is adjusted adaptively to the confidence of depth sensor data. The performance of the proposed method was test by real experiments in various environments. The experimental results showed that the proposed method was capable of reducing the estimation errors and more accurate than the conventional method.

Keywords

Acknowledgement

이 연구는 정부 (과학기술정보통신부)의 재원으로 정보통신기획평가원의 지원을 받아 수행된 지역지능화혁신인재양성사업 (IITP-2024-RS-2020-II201612) 및 산업통상부 재원으로 해양수산과학기술진흥원의 지원을 받아 수행된 연구임 (20210630, 극한지개발 및 탐사용 협동이동체 시스템 기술개발)

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