A Particle Filtering Framework for Integrity Risk of GNSS-Camera Sensor Fusion
This work addresses integrity risk for GNSS-camera fusion in autonomous systems, offering a novel extension of an existing method to a new sensor type, which is incremental in nature.
The paper tackles the problem of joint state estimation and integrity monitoring for GNSS-camera sensor fusion, extending Particle RAIM to include camera data and deriving a Kullback-Leibler Divergence metric to mitigate faults. Experimental results show less than 11 m position error and an integrity risk that overbounds the probability of Hazardously Misleading Information with a 0.11 failure rate for an 8 m Alert Limit in urban scenarios.
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.