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

Adyasha Mohanty, Shubh Gupta and Grace Xingxin Gao

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 [l] 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 [2] 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.
Published in: Proceedings of the 33rd International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2020)
September 21 - 25, 2020
Pages: 2440 - 2452
Cite this article: Mohanty, Adyasha, Gupta, Shubh, Gao, Grace Xingxin, "A Particle Filtering Framework for Integrity Risk of GNSS-Camera Sensor Fusion," Proceedings of the 33rd International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2020), , September 2020, pp. 2440-2452.
https://doi.org/10.33012/2020.17660
Full Paper: ION Members/Non-Members: 1 Download Credit
Sign In