Relative Transformation Estimation Based on Fusion of Odometry and UWB Ranging Data

TL;DR

This paper addresses the estimation of 4-DOF robot relative transformations using odometry and UWB data, introducing optimization methods that outperform existing techniques, especially under challenging conditions.

Contribution

It presents novel QCQP and SDP optimization approaches for relative transformation estimation, including practical considerations like sensor offsets and outlier rejection.

Findings

QCQP method achieves highest accuracy but is computationally intensive.

SDP method offers a good balance between speed and accuracy.

Proposed methods outperform state-of-the-art in simulations and real experiments.

Abstract

In this work, the problem of 4 degree-of-freedom (3D position and heading) robot-to-robot relative frame transformation estimation using onboard odometry and inter-robot distance measurements is studied. Firstly, we present a theoretical analysis of the problem, namely the derivation and interpretation of the Cramer-Rao Lower Bound (CRLB), the Fisher Information Matrix (FIM) and its determinant. Secondly, we propose optimization-based methods to solve the problem, including a quadratically constrained quadratic programming (QCQP) and the corresponding semidefinite programming (SDP) relaxation. Moreover, we address practical issues that are ignored in previous works, such as accounting for spatial-temporal offsets between the ultra-wideband (UWB) and odometry sensors, rejecting UWB outliers and checking for singular configurations before commencing operation. Lastly, extensive simulations and real-life experiments with aerial robots show that the proposed QCQP and SDP methods outperform state-of-the-art methods, especially in geometrically poor or large measurement noise conditions. In general, the QCQP method provides the best results at the expense of computational time, while the SDP method runs much faster and is sufficiently accurate in most cases.