Integrating Deep Reinforcement and Supervised Learning to Expedite Indoor Mapping
This work addresses motion planning inefficiencies in robotics for indoor mapping, offering a domain-specific improvement.
The paper tackles the problem of inefficient indoor mapping by combining deep reinforcement learning with a pre-trained generative neural network as a map predictor, reducing mapping duration by up to 4 times compared to frontier-based methods.
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.