A novel approach to estimating the detailed shape of arbitrary extended objects jointly with their kinematics in the absence of a priori information is presented. The proposed shape model represents the tightest enclosing bound of the object projected into the ego motion plane as a polygon with an unknown number of vertices. Probabilistic inference techniques are employed to overcome various sources of uncertainty by rigorously estimating the joint distribution over the object shape and kinematic states, rather than estimating these variables directly. Simulation and experimental results are presented for objects with complex shapes tracked from an autonomous vehicle research platform. In addition to providing a richer set of information for higher level reasoning about extended objects (e.g. about object type, or occupied space), the results demonstrate that detailed shape estimates enable efficient use of sensor information by way of explicit surface-based sensor models; this efficient use of sensor information improves observability of latent object states, thereby improving tracking precision.

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.

A novel approach to utilize negative information to improve the precision and accuracy of extended multiobject tracking is presented. The parameterized probability density of object tracks undetected in sensor data is updated via inferences about the conditions necessary to result in occlusion of the undetected object. Negative information is also leveraged to inform track existence and data association, both of which contribute to a more sensible belief of the local dynamic scene. Simulation and experimental results are presented from autonomous driving scenarios, demonstrating that the use of negative information leads to a more complete, accurate, precise, and intuitive belief of the local scene, enabling high-level tasks that would otherwise be impractical.

A novel approach to utilizing negative information to improve the accuracy of extended multi-object tracking is presented. The parameterized probability density of object tracks unresolved in sensor data is updated via inferences about the sensor-to-object geometries necessary to result in occlusion of the unresolved object. Negative information is also leveraged to improve data association and to enable a novel death model, all of which contribute to a more accurate and precise belief of the local scene. Simulation and experimental results are presented from a common autonomous driving scenario.