Multi-sensor fusion for steerable four-wheeled industrial vehicles

This paper addresses the problem of multi-sensor data fusion in the navigation of a steerable four-wheeled industrial autonomous vehicle, which experiences substantial load variations of up to twice its weight. The practical considerations in the implementation of the filter an discussed. Tt aims to achieve a robust fusion algorithm with increased system tolerance against prolonged periods when absolute position updates are missing by improving estimation accuracy during dead-reckoning. The main contributions of this paper include the development of an adaptive estimator based on the extended Kalman filler to realise the multi-model filtering; the representation of the vehicle plant using a modified kinematic model to effectively describe the side-slip bias; the processing of redundant measurements to improve system immunity against noisy observations; and the ability to cope with periodically available odometry measurements and temporary position corrections from a landmark-based local reference system. To allow better adaptation to tyre wear and the wheels' deflections under varying loads, the wheel encoder's resolution is constantly calibrated. The filter performance is evaluated at different speeds, loading patterns and maneuvers. Statistical tests are carried out to verify the filter consistency. (C) 1999 Published by Elsevier Science Ltd. All rights reserved.