Reliable Monte Carlo localization for mobile robots

Reliability is a key factor for realizing safety guarantee of fully autonomous robot systems. In this paper, we focus on reliability in mobile localization. Monte Carlo localization (MCL) widely used However, it still difficult to its because there are no methods determining MCL estimate. This paper presents novel framework that enables robust localization, estimation, and quick relocalization, simultaneously. The presented method can be implemented using similar estimation manner MCL. increase robustness environment changes by estimating known unknown obstacles while performing localization; however, failure course occurs unanticipated errors. also includes function know whether has failed. Additionally, the seamlessly integrate global via importance sampling. Consequently, relocalization from state realized mitigating noisy influence We conduct three types experiments wheeled robots equipped with two-dimensional LiDAR. Results show reliable performs self-failure detection, recovery realized.