CSO: Constraint-Guided Space Optimization for Active Scene Mapping

Simultaneously mapping and exploring a complex unknown scene is an NP-hard problem, which is still challenging with the rapid development of deep learning techniques. We present CSO, a deep reinforcement learning-based framework for efficient active scene mapping. Constraint-guided space optimization is adopted for both state and critic space to reduce the difficulty of finding the global optimal explore path and avoid long-distance round trips while exploring. We first take the frontiers-based entropy as the input constraint with the raw observation into the network, which guides the training start from imitating the local greedy searching. However, the entropy-based optimization can easily get stuck with few local optimal or cause inefficient round trips since the entropy space and the real world do not share the same metric. Inspired by constrained reinforcement learning, we then introduce an action mask-based optimization constraint to align the metric of these two spaces. Exploration optimization in aligned spaces can avoid long-distance round trips more effectively. We evaluate our method with a ground robot in 29 complex indoor scenes with different scales. Our method can perform 19.16% more exploration efficiency and 3.12% more exploration completeness on average compared to the state-of-the-art alternatives. We also implement our method in real-world scenes that can efficiently explore an area of 649 m^2. The experiment video can be found in the supplementary material.