Extended Vehicle Tracking with Probabilistic Spatial Relation Projection and Consideration of Shape Feature Uncertainties

This work focuses on a novel probabilistic approach for extended vehicle tracking, where multiple spatially distributed measurements can originate from the target, and kinematic state and geometry variables are estimated jointly. Prominent shape features extracted from raw measurement points contain spatial uncertainties due to noise in sensor measurements, the feature extraction process, approximation error of shape hypothesis, partial vision occlusion, to name a few. This work proposes a novel tracking paradigm that respects the variant spatial measurement model subject to changes in target pose and sensor viewpoint. This is achieved through probabilistic projection of the spatial measurement points to the predicted measurement sources on the visible side(s) of the target shape. The spatial uncertainties in the shape features are probabilistically modelled and incorporated in the unscented Kalman filter based estimation. The proposed approach is validated with field experiment results using cameras and a laser range scanner.