The problem of quantifying robot localization safety in the presence of undetected sensor faults is critical when preparing for future applications where robots may interact with humans in life-critical situations; however, the topic is only sparsely addressed in the robotics literature. In response, this work leverages prior work in aviation integrity monitoring to tackle the more challenging case of evaluating localization safety in Global Navigation Satellite System (GNSS)-denied environments. Localization integrity risk is the probability that a robot’s pose estimate lies outside pre-defined acceptable limits while no alarm is triggered. In this article, the integrity risk (i.e., localization safety) is rigorously upper bounded by accounting for both nominal sensor noise and other non-nominal sensor faults. An extended Kalman filter is employed to estimate the robot state, and a sequence of innovations is used for fault detection. The novelty of the work includes (1) the use of a time window to limit the number of monitored fault hypotheses while still guaranteeing safety with respect to previously occurring faults and (2) a new method to account for faults in the data association process.

中文翻译：

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基于卡尔曼滤波器的定位的完整性监控

在为机器人可能在生命攸关的情况下与人类互动的未来应用做准备时，在存在未检测到的传感器故障的情况下量化机器人定位安全性的问题至关重要；然而，这个话题在机器人文献中很少涉及。作为回应，这项工作利用先前在航空完整性监测方面的工作来解决在全球导航卫星系统 (GNSS) 拒绝环境中评估定位安全的更具挑战性的案例。定位完整性风险是机器人的姿态估计在未触发警报的情况下超出预先定义的可接受限制的概率。在本文中，完整性风险（即定位安全性）通过考虑标称传感器噪声和其他非标称传感器故障来严格设定上限。采用扩展卡尔曼滤波器来估计机器人状态，并使用一系列创新进行故障检测。这项工作的新颖之处包括 (1) 使用时间窗口来限制受监控故障假设的数量，同时仍然保证先前发生的故障的安全性和 (2) 一种解释数据关联过程中故障的新方法。