State Estimation of Articulated Vehicles Using Deformed Superellipses

State estimation of objects plays an important role in various kinds of applications in the fields of robotics and autonomous vehicles. With the continuous advancement of sensors with high spatial resolution, especially light detection and ranging (LiDAR), the interest in accurate and reliable extended object trackers has grown over the last years. Classical state estimation approaches assume static and symmetric shapes, such as rectangles or ellipses, or compositions of those. The disadvantage of that assumption is obvious: deformations, as in the case of articulated vehicles driving along curves, cannot be captured appropriately. In this paper, we tackle this problem by proposing a novel approach to state estimation employing deformed superellipses. This allows a closed-form mathematical description of an articulated object’s state in the Euclidean plane consisting of its pose and shape. Two additional state parameters are introduced capturing the deformation angle and the joint’s position. We evaluate the proposed approach to state estimation of articulated objects employing a model fitting algorithm of simulated LiDAR measurements and show the improvements compared to classical shape assumptions. Furthermore, we discuss the use of our approach in a tracking algorithm.