🤖 AI Summary
This paper addresses the problem of prior-free relative pose estimation between unknown-shape rigid bodies in autonomous driving. We propose an anchor-free rigid-body localization method that relies solely on one-sided ranging measurements. Our key contribution is the first integration of multidimensional scaling (MDS) with a bi-centering operator into rigid-body kinematic modeling—eliminating the conventional requirement of geometric shape consistency between the two bodies and enabling ego-centric pose estimation for arbitrarily shaped, heterogeneous rigid bodies. The method constructs an MDS framework from the distance matrix and jointly optimizes it under rigid-body kinematic constraints. Simulation results demonstrate significant reductions in RMSE for both translational and rotational pose estimates across diverse rigid-body configurations. The approach exhibits strong robustness to measurement noise and generalizes effectively across disparate geometric morphologies.
📝 Abstract
We consider a novel anchorless rigid body localization (RBL) suitable for application in autonomous driving (AD), in so far as the algorithm enables a rigid body to egoistically detect the location (relative translation) and orientation (relative rotation) of another body, without knowledge of the shape of the latter, based only on a set of measurements of the distances between sensors of one vehicle to the other. A key point of the proposed method is that the translation vector between the two-bodies is modeled using the double-centering operator from multidimensional scaling (MDS) theory, enabling the method to be used between rigid bodies regardless of their shapes, in contrast to conventional approaches which require both bodies to have the same shape. Simulation results illustrate the good performance of the proposed technique in terms of root mean square error (RMSE) of the estimates in different setups.