Enhanced State Estimation for Wheeled Vehicles

Paul F. Roysdon, Jay A. Farrell, David Kelley

Peer Reviewed

Abstract:	Accurate state estimation for wheeled vehicles is valuable for a wide variety of applications from vehicle stability control to life-safety critical systems. Commonly applied GNSS-encoder state estimation approaches like the Extended Kalman Filter can be challenged when addressing the issue of significantly corrupted data, due to limited measurement redundancy. One approach to increasing measurement redundancy is to include additional sensors like Inertial Measurement Units (IMU’s), cameras and LiDARs. This paper presents an alternative approach, leveraging a minimal set of sensors which are already installed on most modern mass-production vehicles, and requiring only a software update. We propose a sliding window, nonlinear, Maximum-a-Posteriori estimator to solve the GNSS-encoder navigation problem. The sliding window implementation provides sufficient redundancy to enable detection and compensation of various measurement anomalies. While this paper focuses on automotive applications, this method applies to any wheeled vehicle (e.g. rovers, trains, commercial equipment).
Published in:	Proceedings of the 28th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2015) September 14 - 18, 2015 Tampa Convention Center Tampa, Florida
Pages:	2275 - 2281
Cite this article:	Roysdon, Paul F., Farrell, Jay A., Kelley, David, "Enhanced State Estimation for Wheeled Vehicles," Proceedings of the 28th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2015), Tampa, Florida, September 2015, pp. 2275-2281.
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