Egoistic MDS-based Rigid Body Localization

TL;DR

This paper introduces a novel anchorless rigid body localization method for autonomous driving that estimates relative position and orientation without shape knowledge, using MDS-based double-centering for shape-agnostic translation estimation.

Contribution

The paper proposes a shape-independent rigid body localization technique based on multidimensional scaling, enabling egoistic detection of relative pose without shape constraints.

Findings

Good RMSE performance demonstrated in simulations

Shape-agnostic localization achieved

Applicable to autonomous driving scenarios

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.