Adaptive state estimation for 4-wheel steerable industrial vehicles

Addresses the multi-sensor data fusion problem in the navigation of a four-wheel steerable industrial vehicle with substantial load variations. An adaptive estimation approach based on the extended Kalman filter is used to realise the multi-model filtering. The vehicle plant is represented using a modified kinematic model to effectively describe the side-slip which causes the vehicle to deviate from its ideal course. In view of the large mass variations and wheels´ deflections, a method to constantly calibrate the odometry encoder´s resolution is proposed to maintain an accurate position report even with long dead-reckoning distance. The position measurements from a landmark-based local reference system are fused with the odometry measurements to provide an optimal estimate of the vehicle´s states. The filter performance is evaluated at different speeds and loading patterns using data obtained from field trials