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

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.