Integrity risk is a measure of localization safety that accounts for the presence of undetected sensor faults. The metric has been used for decades in aviation and has recently been applied to terrestrial robots operating on life-critical missions. For ground vehicles, integrity risk can be quantified for systems using lidar measurements, where two specific fault types have been identified: miss-association and unmapped association. While miss-association faults, which occur when a correctly extracted feature is associated with the wrong landmark, have been well studied, the probability of an unmapped association fault, where an incorrectly extracted feature is associated with a landmark, is not well understood. Namely, previous research has never quantified this value and instead relies on an assumed value, one whose value has not been properly justified. This work is the first to provide a methodology that estimates the risk of unmapped association for each mapped landmark; the article demonstrates the effect of this probability for both the chi-squared and fixed-lag smoothing methods for integrity monitoring. Data collected in downtown Chicago, IL, USA, were used to test the impact of unmapped association faults on localization safety. The results indicate that using the previously assumed value is reasonable in many situations, but that applications with strict safety requirements should incorporate the method described here to properly account for unmapped association faults.

中文翻译：

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量化移动机器人定位安全的未映射关联的风险


完整性风险是一种本地化安全性衡量标准，用于解释未检测到的传感器故障的存在。该指标已在航空领域使用了数十年，最近还应用于执行生命攸关任务的地面机器人。对于地面车辆，可以使用激光雷达测量来量化系统的完整性风险，其中已识别出两种特定的故障类型：错误关联和未映射关联。虽然错误关联故障（当正确提取的特征与错误的地标相关联时发生）已得到充分研究，但未映射的关联故障（其中错误提取的特征与地标相关联）的概率尚不清楚。也就是说，以前的研究从未量化过该值，而是依赖于一个假设值，而该值尚未得到适当证明。这项工作首次提供了一种方法来估计每个已映射地标的未映射关联的风险；该文章演示了这种概率对于完整性监控的卡方和固定滞后平滑方法的影响。在美国伊利诺伊州芝加哥市中心收集的数据用于测试未映射的关联故障对定位安全的影响。结果表明，在许多情况下使用先前假设的值是合理的，但具有严格安全要求的应用程序应结合此处描述的方法，以正确解释未映射的关联故障。