Policies over Poses: Reinforcement Learning based Distributed Pose-Graph Optimization for Multi-Robot SLAM

• URL: http://arxiv.org/abs/2510.22740v1
• Date: Sun, 26 Oct 2025 16:21:24 GMT
• Title: Policies over Poses: Reinforcement Learning based Distributed Pose-Graph Optimization for Multi-Robot SLAM
• Authors: Sai Krishna Ghanta, Ramviyas Parasuraman,
• Abstract summary: We consider the sequential distributed pose-graph optimization (PGO) in multi-robot localization.<n>We cast PGO as a partially observable game defined on a Markov Neural Network (GNN), where each action refines a single edge's pose estimate.<n>We show that our learned actors reduce the global trajectory by an average of 37.5%, while enhancing efficiency by at least 6X.
• Score: 1.3750624267664158
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We consider the distributed pose-graph optimization (PGO) problem, which is fundamental in accurate trajectory estimation in multi-robot simultaneous localization and mapping (SLAM). Conventional iterative approaches linearize a highly non-convex optimization objective, requiring repeated solving of normal equations, which often converge to local minima and thus produce suboptimal estimates. We propose a scalable, outlier-robust distributed planar PGO framework using Multi-Agent Reinforcement Learning (MARL). We cast distributed PGO as a partially observable Markov game defined on local pose-graphs, where each action refines a single edge's pose estimate. A graph partitioner decomposes the global pose graph, and each robot runs a recurrent edge-conditioned Graph Neural Network (GNN) encoder with adaptive edge-gating to denoise noisy edges. Robots sequentially refine poses through a hybrid policy that utilizes prior action memory and graph embeddings. After local graph correction, a consensus scheme reconciles inter-robot disagreements to produce a globally consistent estimate. Our extensive evaluations on a comprehensive suite of synthetic and real-world datasets demonstrate that our learned MARL-based actors reduce the global objective by an average of 37.5% more than the state-of-the-art distributed PGO framework, while enhancing inference efficiency by at least 6X. We also demonstrate that actor replication allows a single learned policy to scale effortlessly to substantially larger robot teams without any retraining. Code is publicly available at https://github.com/herolab-uga/policies-over-poses.

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