RL-PGO: Reinforcement Learning-based Planar Pose-Graph Optimization

• URL: http://arxiv.org/abs/2202.13221v1
• Date: Sat, 26 Feb 2022 20:10:14 GMT
• Title: RL-PGO: Reinforcement Learning-based Planar Pose-Graph Optimization
• Authors: Nikolaos Kourtzanidis, Sajad Saeedi
• Abstract summary: This paper presents a state-of-the-art Deep Reinforcement Learning (DRL) based environment and proposed agent for 2D pose-graph optimization. We demonstrate that the pose-graph optimization problem can be modeled as a partially observable Decision Process and evaluate performance on real-world and synthetic datasets.
• Score: 1.4884785898657995
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The objective of pose SLAM or pose-graph optimization (PGO) is to estimate the trajectory of a robot given odometric and loop closing constraints. State-of-the-art iterative approaches typically involve the linearization of a non-convex objective function and then repeatedly solve a set of normal equations. Furthermore, these methods may converge to a local minima yielding sub-optimal results. In this work, we present to the best of our knowledge the first Deep Reinforcement Learning (DRL) based environment and proposed agent for 2D pose-graph optimization. We demonstrate that the pose-graph optimization problem can be modeled as a partially observable Markov Decision Process and evaluate performance on real-world and synthetic datasets. The proposed agent outperforms state-of-the-art solver g2o on challenging instances where traditional nonlinear least-squares techniques may fail or converge to unsatisfactory solutions. Experimental results indicate that iterative-based solvers bootstrapped with the proposed approach allow for significantly higher quality estimations. We believe that reinforcement learning-based PGO is a promising avenue to further accelerate research towards globally optimal algorithms. Thus, our work paves the way to new optimization strategies in the 2D pose SLAM domain.

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