Bearing-based Relative Localization for Robotic Swarm with Partially Mutual Observations

TL;DR

This paper introduces a robust, scalable algorithm for relative localization in robotic swarms with partial mutual observations, combining optimization techniques and real-world validation.

Contribution

It presents a novel complete algorithm that guarantees optimality and scalability for relative pose estimation with partial mutual observations in robot swarms.

Findings

The method achieves global optimality in simulations.

It demonstrates robustness under various noise levels.

Real-world experiments confirm practicality and robustness.

Abstract

Mutual localization provides a consensus of reference frame as an essential basis for cooperation in multirobot systems. Previous works have developed certifiable and robust solvers for relative transformation estimation between each pair of robots. However, recovering relative poses for robotic swarm with partially mutual observations is still an unexploited problem. In this paper, we present a complete algorithm for it with optimality, scalability and robustness. Firstly, we fuse all odometry and bearing measurements in a unified minimization problem among the Stiefel manifold. Furthermore, we relax the original non-convex problem into a semi-definite programming (SDP) problem with a strict tightness guarantee. Then, to hold the exactness in noised cases, we add a convex (linear) rank cost and apply a convex iteration algorithm. We compare our approach with local optimization methods on extensive simulations with different robot amounts under various noise levels to show our global optimality and scalability advantage. Finally, we conduct real-world experiments to show the practicality and robustness.