Joint tracking and non-parametric shape estimation of arbitrary extended objects

This paper presents a probabilistically rigorous method for jointly estimating the shape and kinematic states of arbitrary extended objects. A non-parametric shape model is defined as a set of points sampled from the object surface, and the joint probability density function over the surface samples is estimated recursively over time from lidar data. The presented work is demonstrated for a single maneuvering, non-convex object, highlighting key advantages over existing methods and motivating further development.