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

In this letter, we present to the best of our knowledge, the first deep reinforcement learning (DRL) based 2D pose-graph optimization (PGO). We demonstrate that the pose-graph optimization problem can be modeled as a partially observable Markov Decision Process. The proposed agent outperforms state-of-the-art solver
$\mathrm {g}^{2} \mathrm {o}$
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.