Overlapping Domain Decomposition for Distributed Pose Graph Optimization

TL;DR

ROBO is a distributed pose graph optimization method that uses overlapping domain decomposition to reduce convergence time, balancing communication and computation for multi-robot systems.

Contribution

The paper introduces ROBO, a novel overlapping domain decomposition approach for distributed pose graph optimization, improving convergence speed and robustness in multi-robot scenarios.

Findings

ROBO converges 3.1 times faster than existing methods.

Overlapping blocks with 36 Kb data per iteration are effective.

ROBO's asynchronous variant handles network delays robustly.

Abstract

We present ROBO (Riemannian Overlapping Block Optimization), a distributed and parallel approach to multi-robot pose graph optimization (PGO) based on the idea of overlapping domain decomposition. ROBO offers a middle ground between centralized and fully distributed solvers, where the amount of pose information shared between robots at each optimization iteration can be set according to the available communication resources. Sharing additional pose information between neighboring robots effectively creates overlapping optimization blocks in the underlying pose graph, which substantially reduces the number of iterations required to converge. Through extensive experiments on benchmark PGO datasets, we demonstrate the applicability and feasibility of ROBO in different initialization scenarios, using various cost functions, and under different communication regimes. We also analyze the tradeoff between the increased communication and local computation required by ROBO's overlapping blocks and the resulting faster convergence. We show that overlaps with an average inter-robot data cost of only 36 Kb per iteration can converge 3.1$\times$ faster in terms of iterations than state-of-the-art distributed PGO approaches. Furthermore, we develop an asynchronous variant of ROBO that is robust to network delays and suitable for real-world robotic applications.