A particle-filtering framework for integrity risk of GNSS-camera sensor fusion

Abstract

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.