Technology Category
Constraint Satisfaction and Optimization: Distributed CSP/OptimizationSearch and Optimization: Distributed SearchIntelligent Robots: Learning & Optimization for ROB
Overlapping Domain Decomposition for Distributed Pose Graph Optimization
🤖 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
Application Category
📝 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
Research questions and friction points this paper is trying to address.
Pose Graph Optimization
Distributed Optimization
Multi-robot Systems
Domain Decomposition
Communication Constraints
Innovation
Methods, ideas, or system contributions that make the work stand out.
overlapping domain decomposition
distributed pose graph optimization
Riemannian optimization
multi-robot systems
asynchronous optimization
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