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.

翻译：可靠性是实现完全自主机器人系统安全保障的一个关键因素。 在本文中, 我们关注移动机器人本地化的可靠性。 Monte Carlo 本地化( MCL) 被广泛用于移动机器人本地化。 但是, 由于缺乏确定 MCL 估算可靠性的方法, 仍难以保障其安全。 本文同时展示了一个新的本地化框架, 能够实现稳健的本地化、 可靠性估计和快速重新本地化。 所提出的方法可以使用与 MCL 类似的估算方法实施。 该方法可以通过估算已知和未知障碍来增强本地化对环境变化的稳健性; 但是, 课程本地化失败是由意外错误造成的。 该方法还包括一个可靠性估算功能, 使我们能够知道本地化是否失败。 此外, 该方法可以通过重要取样将全球本地化方法无缝合。 因此, 在减少全球本地化的噪音影响的同时, 快速本地化可以实现。 通过三种类型的实验, 我们证明可靠的 MCLL 能够实现可靠的本地化, 进行稳健的本地化、 自失能和快速故障恢复。