In this paper, we consider the problem of tracking dynamic objects with unknown shapes using point cloud measurements generated by sensors such as lidars and radars. Specifically, our objective is to extend the Gaussian process-based extended object tracking (GPEOT) framework to encompass a broader class of objects. The derivation of the existing GPEOT algorithms is based on the assumption that the object of interest is star-convex. This assumption enables the modeling of the object’s extent through a radial distance function, which is described by a Gaussian process (GP). To enhance the flexibility of the resulting trackers, we propose the utilization of a potential function to indicate the unknown object extent. This approach enables the representation of objects with arbitrary shapes, including those that are non-convex and composed of disconnected components. Closely following the original formulation of GPEOT, the potential function is then modeled by a GP, which systematically accounts for the intrinsic spatial correlation of the extent. Furthermore, we develop a state-space model that incorporates both kinematic variables and an approximate description of the underlying GP model. The state vector can be estimated via a standard Bayesian technique, leading to an EOT algorithm. Through simulation experiments, we demonstrate the suggested method can satisfactorily estimate the kinematic variables of the objects while simultaneously learning their complex shapes.

In this work, we propose a new method to track extended targets of different shapes such as ellipses, rectangles and rhombi. We provide an analytical framework to express these shapes as superelliptical contours and propose a Bayesian filtering scheme that can handle measurements from the contour of the object. The method utilizes the Rao-Blackwellized particle filtering algorithm with novel sensor-object geometry constraints. The success of the algorithm is demonstrated using both simulations and real-data experiments, and the algorithm has been demonstrated to be of high performance in various challenging scenarios.

In this paper, we consider the problem of extended target tracking, where the target extent cannot be represented by a single ellipse accurately. We model the target extent with multiple ellipses and solve the resulting inference problem, which involves data association between the measurements and sub-objects. We cast the inference problem into sequential Monte Carlo (SMC) framework and propose a simplified approach for the solution. Furthermore, we make use of the Rao-Blackwellization, aka marginalization, idea and derive an efficient filter to approximate the joint posterior density of the target kinematic states and target extent. Conditional analytical expressions, which are essential for Rao-Blackwellization, are not available in our problem. We use variational Bayes technique to approximate the conditional densities and enable Rao-Blackwellization. The performance of the method is demonstrated through simulations. A comparison with a recent method in the literature is performed.

In this study, we consider the challenging task of tracking dynamic 3D objects with unknown shapes by using sparse point cloud measurements gathered from the surface of the objects. We propose a Gaussian process based algorithm that is capable of tracking the dynamic behavior of the object and learn its shape in 3D simultaneously. Our solution does not require any parametric model assumption for the unknown shape. The shape of the objects is learned online via a Gaussian process. The proposed method can jointly estimate the position, orientation, and the shape of the object. The inference is performed by an extended Kalman filter which is suitable for online real-time applications. Lastly, we demonstrate the initial results of a promising approach, which aims at reducing the computational complexity.

Recent extended target tracking algorithms provide reliable shape estimates while tracking objects. The estimated extent of the objects can also be used for online classification. In this work, we propose to use a Bayesian classifier to identify different objects based on their contour estimates during tracking. The proposed method uses the uncertainty information provided by the estimation covariance of the tracker.