  Reliability is a key factor for realizing safety guarantee of full 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 re-localization, simultaneously. The
presented method can be implemented using 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 us to know whether
localization has failed. Additionally, the method can seamlessly integrate a
global localization method via importance sampling. Consequently, quick
re-localization from failures can be realized while mitigating noisy influence
of global localization. Through three types of experiments, we show that
reliable MCL that performs robust localization, self-failure detection, and
quick failure recovery can be realized.
