Geometric Data Fusion for Collaborative Attitude Estimation

TL;DR

This paper introduces a geometric data fusion approach for collaborative attitude estimation in multi-agent systems, combining local EKF estimates with relative measurements to improve accuracy and avoid data incest.

Contribution

It proposes a novel geometric correction and fusion method for multi-agent attitude estimation using EKF and convex combination ellipsoid techniques.

Findings

Enhanced estimation accuracy demonstrated in simulations

Effective covariance correction for relative measurements

Robustness against data incest in multi-agent systems

Abstract

In this paper, we consider the collaborative attitude estimation problem for a multi-agent system. The agents are equipped with sensors that provide directional measurements and relative attitude measurements. We present a bottom-up approach where each agent runs an extended Kalman filter (EKF) locally using directional measurements and augments this with relative attitude measurements provided by neighbouring agents. The covariance estimates of the relative attitude measurements are geometrically corrected to compensate for relative attitude between the agent that makes the measurement and the agent that uses the measurement before being fused with the local estimate using the convex combination ellipsoid (CCE) method to avoid data incest. Simulations are undertaken to numerically evaluate the performance of the proposed algorithm.