  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.
