Adopting a joint approach toward state estimation and integrity monitoring results in unbiased integrity monitoring unlike traditional approaches. So far, a joint approach was used in particle RAIM (Gupta & Gao, 2019) for GNSS measurements only. In our work, we extend Particle RAIM to a GNSS-camera fused system for joint state estimation and integrity monitoring. To account for vision faults, we derived a probability distribution over position from camera images using map-matching. We formulated a Kullback-Leibler divergence (Kullback & Leibler, 1951) metric to assess the consistency of GNSS and camera measurements and mitigate faults during sensor fusion. Experimental validation on a real-world data set shows that our algorithm produces less than 11 m position error and the integrity risk over bounds the probability of HMI with 0.11 failure rate for an 8 m alert limit in an urban scenario.

中文翻译：

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GNSS-相机传感器融合完整性风险的粒子过滤框架

与传统方法不同，对状态估计和完整性监控采用联合方法可以实现无偏见的完整性监控。到目前为止，粒子 RAIM (Gupta & Gao, 2019) 中使用了一种联合方法，仅用于 GNSS 测量。在我们的工作中，我们将 Particle RAIM 扩展到 GNSS 相机融合系统，用于联合状态估计和完整性监控。为了解决视觉错误，我们使用地图匹配从相机图像中推导出位置的概率分布。我们制定了 Kullback-Leibler 散度 (Kullback & Leibler, 1951) 度量标准来评估 GNSS 和相机测量的一致性，并减轻传感器融合过程中的故障。对真实世界数据集的实验验证表明，我们的算法产生的位置误差小于 11 m，并且完整性风险超过了 HMI 的概率为 0。