Integrating Deep Reinforcement and Supervised Learning to Expedite Indoor Mapping

TL;DR

This paper introduces a novel indoor mapping approach combining deep reinforcement learning and a generative neural network to significantly reduce mapping time by leveraging environment statistics.

Contribution

It proposes integrating deep reinforcement learning with a pre-trained generative neural network to improve indoor mapping efficiency and decision-making speed.

Findings

Mapping duration reduced by up to 4 times

Neural network-based methods ensure constant calculation time

Improved decision-making using learned environment statistics

Abstract

The challenge of mapping indoor environments is addressed. Typical heuristic algorithms for solving the motion planning problem are frontier-based methods, that are especially effective when the environment is completely unknown. However, in cases where prior statistical data on the environment's architectonic features is available, such algorithms can be far from optimal. Furthermore, their calculation time may increase substantially as more areas are exposed. In this paper we propose two means by which to overcome these shortcomings. One is the use of deep reinforcement learning to train the motion planner. The second is the inclusion of a pre-trained generative deep neural network, acting as a map predictor. Each one helps to improve the decision making through use of the learned structural statistics of the environment, and both, being realized as neural networks, ensure a constant calculation time. We show that combining the two methods can shorten the duration of the mapping process by up to 4 times, compared to frontier-based motion planning.