Reliable Monte Carlo localization for mobile robots

被引:13
作者
Akai, Naoki [1 ,2 ,3 ]
机构
[1] Nagoya Univ, Grad Sch Engn, Grad Dept Aerosp Engn, Nagoya, Japan
[2] LOCT Co Ltd, Nagoya, Japan
[3] Nagoya Univ, Grad Sch Engn, Chikusa Ku, Nagoya, Japan
基金
日本科学技术振兴机构;
关键词
localization; mobile robots; modeling; probabilistic; reliability; REGISTRATION;
D O I
10.1002/rob.22149
中图分类号
TP24 [机器人技术];
学科分类号
080202 ; 1405 ;
摘要
Reliability is a key factor for realizing safety guarantee of fully autonomous robot systems. In this paper, we focus on reliability in mobile robot localization. Monte Carlo localization (MCL) is widely used for mobile robot localization. However, it is still difficult to guarantee its safety because there are no methods determining reliability for MCL estimate. This paper presents a novel localization framework that enables robust localization, reliability estimation, and quick relocalization, simultaneously. The presented method can be implemented using a similar estimation manner to that of MCL. The method can increase localization robustness to environment changes by estimating known and unknown obstacles while performing localization; however, localization failure of course occurs by unanticipated errors. The method also includes a reliability estimation function that enables a robot to know whether localization has failed. Additionally, the method can seamlessly integrate a global localization method via importance sampling. Consequently, quick relocalization from a failure state can be realized while mitigating noisy influence of global localization. We conduct three types of experiments using wheeled mobile robots equipped with a two-dimensional LiDAR. Results show that reliable MCL that performs robust localization, self-failure detection, and quick failure recovery can be realized.
引用
收藏
页码:595 / 613
页数:19
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