Deriving safe bounds on particle filter estimate is a research problem that, if solved, could greatly benefit robots in life-critical applications, a field that is facing increasing interest as more robots are being deployed near humans. In response, this article introduces a new fault detector and derives a performance measure for particle filter: integrity risk. Integrity risk is defined as the probability of having large estimate errors without triggering an alarm, all while considering measurement faults, unknown deterministic errors that cannot be modeled via normal white noise. In this work, the faults come in the form of incorrectly associated features when using the local nearest neighbors. Simulations and experiments assess the efficiency of the introduced safety metric. The results show that safety improves as map density increases as long as the number of particles is sufficient to shape the error distribution and the landmarks are well separated. Also, the results indicate that, when landmarks are poorly separated, particle filter is safer than Kalman filter, whereas, when landmarks are well separated, particle filter is often, but not always, safer than Kalman filter.

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基于粒子过滤的移动机器人定位有多安全？完整性监控方法


推导粒子滤波器估计的安全范围是一个研究问题，如果解决的话，可以极大地使机器人在生命攸关的应用中受益，随着越来越多的机器人被部署在人类附近，这一领域正面临着越来越多的关注。作为回应，本文介绍了一种新的故障检测器，并推导了颗粒过滤器的性能衡量标准：完整性风险。完整性风险被定义为在不触发警报的情况下出现较大估计误差的概率，同时考虑测量故障、无法通过正常白噪声建模的未知确定性误差。在这项工作中，当使用本地最近邻居时，错误以错误关联的特征的形式出现。模拟和实验评估了引入的安全指标的效率。结果表明，只要粒子数量足以塑造误差分布并且地标能够很好地分离，随着地图密度的增加，安全性也会提高。此外，结果表明，当地标分离不良时，粒子滤波器比卡尔曼滤波器更安全，而当地标分离良好时，粒子滤波器通常（但并非总是）比卡尔曼滤波器更安全。