Integrity monitoring for Kalman filter-based localization
Integrity monitoring for Kalman filter-based localization
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DOI：
10.1177/0278364920960517
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发表时间：
2020-11-01
影响因子：
9.2
通讯作者：
Spenko, Matthew
中科院分区：
文献类型：
--
作者：
Arana, Guillermo Duenas;Hafez, Osama Abdul;Spenko, Matthew
在为机器人可能在危及生命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，这个主题在机器人文献中很少涉及。作为回应，这项工作利用了航空完整性监测方面的先前工作来解决评估全球导航卫星系统（GNSS）拒绝环境中的定位安全性这一更具挑战性的案例。定位完整性风险是指在没有触发警报的情况下机器人的姿态估计超出预定义的可接受限制的概率。在本文中，通过考虑标称传感器噪声和其他非标称传感器故障，完整性风险（即定位安全）严格受到上限限制。采用扩展卡尔曼滤波器来估计机器人状态，并使用一系列创新来进行故障检测。这项工作的新颖性包括（1）使用时间窗口来限制监视的故障假设的数量，同时仍然保证先前发生的故障的安全性；（2）一种解释数据关联过程中的故障的新方法。
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.