A Particle Filtering Framework for Integrity Risk of GNSS-Camera Sensor Fusion

TL;DR

This paper introduces a novel particle filtering framework that fuses GNSS and camera data for improved state estimation and integrity monitoring, effectively detecting faults and bounding the risk of hazardous misinformation in urban environments.

Contribution

It extends Particle RAIM to a GNSS-camera fusion system, incorporating map-matching and divergence metrics for joint integrity monitoring, which was not previously done.

Findings

Position error less than 11 meters in experiments

Integrity risk bounds the probability of HMI at 0.11 failure rate

Effective fault detection in urban scenarios

Abstract

Adopting a joint approach towards state estimation and integrity monitoring results in unbiased integrity monitoring unlike traditional approaches. So far, a joint approach was used in Particle RAIM [l] 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 derive a probability distribution over position from camera images using map-matching. We formulate a Kullback-Leibler Divergence metric to assess the consistency of GNSS and camera measurements and mitigate faults during sensor fusion. The derived integrity risk upper bounds the probability of Hazardously Misleading Information (HMI). Experimental validation on a real-world dataset 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.