Overlapping Domain Decomposition for Distributed Pose Graph Optimization

2026-03-03

AI Summary

This work addresses the trade-off between slow convergence and high communication overhead in distributed pose graph optimization for multi-robot systems by proposing a semi-distributed approach based on overlapping domain decomposition. By constructing adjustable overlapping optimization blocks, the method enables controlled sharing of pose information among neighboring robots, significantly accelerating convergence. Built upon the Riemannian Overlapping Block Optimization (ROBO) framework, it integrates graph decomposition, distributed nonlinear optimization, and asynchronous communication, exhibiting robustness to network latency. Experimental results on standard datasets demonstrate that the proposed method achieves a 3.1× faster convergence rate compared to existing distributed approaches while requiring only an average communication cost of 36 KB per iteration.

Technology Category

• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Search and Optimization: Distributed Search
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies

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.

Problem

• Pose Graph Optimization
• Distributed Optimization
• Multi-robot Systems
• Domain Decomposition
• Communication Constraints

Innovation

• overlapping domain decomposition
• distributed pose graph optimization
• Riemannian optimization
• multi-robot systems
• asynchronous optimization