Rigid Body Localization via Gaussian Belief Propagation with Quadratic Angle Approximation

TL;DR

This paper introduces a novel Gaussian belief propagation-based rigid body localization method that uses quadratic angle approximation to accurately estimate orientation without prior orientation knowledge, achieving high precision with low complexity.

Contribution

The paper presents a new GaBP-based RBL scheme that removes the need for prior orientation information by incorporating quadratic angle approximation for better accuracy.

Findings

Accurate rotation angle estimates even for large deviations.

Comparable or improved RMSE performance versus state-of-the-art methods.

Maintains low computational complexity.

Abstract

Gaussian belief propagation (GaBP) is a technique that relies on linearized error and input-output models to yield low-complexity solutions to complex estimation problems, which has been recently shown to be effective in the design of range-based GaBP schemes for stationary and moving rigid body localization (RBL) in three-dimensional (3D) space, as long as an accurate prior on the orientation of the target rigid body is available. In this article we present a novel range-based RBL scheme via GaBP that removes the latter limitation. To this end, the proposed method incorporates a quadratic angle approximation to linearize the relative orientation between the prior and the target rigid body, enabling high precision estimates of corresponding rotation angles even for large deviations. Leveraging the resulting linearized model, we derive the corresponding message-passing (MP) rules to obtain estimates of the translation vector and rotation matrix of the target rigid body, relative to a prior reference frame. Numerical results corroborate the good performance of the proposed angle approximation itself, as well as the consequent RBL performance in terms of root mean square errors (RMSEs) in comparison to the state-of-the-art (SotA), while maintaining a low computational complexity