  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.
