Getting The Best of Particle and Kalman Filters: GNSS Sensor Fusion using Rao-Blackwellized Particle Filter

Shubh Gupta, Adyasha Mohanty, and Grace Gao

Abstract:	In this paper, we develop a hybrid Bayesian filter for GNSS and visual odometry (VO) fusion in urban environments that combines the tracking efficiency of Kalman filter with the superior uncertainty modeling of particle filter. Our filter design employs Rao-Blackwellization to decouple the state into a non-linearly tracked position and linearly tracked orientation, velocity and carrier phase integer ambiguities. This factorization allows our filter to efficiently track the state along with a rich probability distribution of the position. Moreover, we utilize the tracked position probability distribution to quantify the uncertainty in situations where the tracking is erroneous. We evaluate our approach for state and uncertainty estimation using GNSS-VO fusion on real-world data and demonstrate that our filter exhibits comparable computation efficiency and improved state and uncertainty estimates over other Bayesian filter baselines.
Published in:	Proceedings of the 35th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2022) September 19 - 23, 2022 Hyatt Regency Denver Denver, Colorado
Pages:	1610 - 1623
Cite this article:	Gupta, Shubh, Mohanty, Adyasha, Gao, Grace, "Getting The Best of Particle and Kalman Filters: GNSS Sensor Fusion using Rao-Blackwellized Particle Filter," Proceedings of the 35th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2022), Denver, Colorado, September 2022, pp. 1610-1623. https://doi.org/10.33012/2022.18470
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