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首页> 外文期刊>The International journal of robotics research >Integrity monitoring for Kalman filter-based localization
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Integrity monitoring for Kalman filter-based localization

机译：基于卡尔曼滤波器的本地化的完整性监控

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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.
机译：在未调整的传感器故障存在下量化机器人定位安全性的问题对于为未来的应用程序准备机器人可以与人类在生活中的情况相互作用时，这是至关重要的;但是，该主题仅在机器人文学中稀疏地解决。作为回应，这项工作利用了航空完整性监测的先前工作，以解决评估全球导航卫星系统（GNSS）的本地化安全的更具挑战性的情况。本地化完整性风险是机器人的姿势估计的概率在于在外部预定定义的可接受限制，而没有触发警报。在本文中，完整性风险（即，本地化安全）通过算起标称传感器噪声和其他非名义传感器故障而严格的上限。扩展卡尔曼滤波器用于估计机器人状态，并且一系列创新用于故障检测。该工作的新颖性包括（1）使用时间窗口来限制受监控故障假设的数量，同时仍然保证了以前发生的故障和（2）一种用于数据关联过程中故障的新方法。

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