论文标题
可靠的移动机器人蒙特卡洛本地化
Reliable Monte Carlo Localization for Mobile Robots
论文作者
论文摘要
可靠性是实现完整自动驾驶机器人系统安全保证的关键因素。在本文中，我们专注于移动机器人本地化的可靠性。蒙特卡洛定位（MCL）广泛用于移动机器人定位。但是，仍然很难确保其安全性，因为没有方法可以确定MCL估计的可靠性。本文介绍了一个新的本地化框架，可以同时实现强大的本地化，可靠性估计和快速重新定位。可以使用与MCL相似的估计方式实现了所提出的方法。该方法可以通过在执行本地化时估计已知和未知障碍来提高对环境变化的稳健性。但是，本地化失败当然是由于意外错误而发生的。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性抽样无缝地整合全局定位方法。因此，可以在减轻全球本地化的嘈杂影响时实现失败的快速重新定位。通过三种类型的实验，我们表明可以实现可靠的MCL，可以实现强大的本地化，自发检测和快速故障恢复。
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.
