Learning-Augmented Model-Based Planning for Visual Exploration
This work addresses robotic exploration for autonomous systems in indoor environments, representing an incremental advance with specific performance gains.
The paper tackles the problem of time-limited robotic exploration in unseen environments by proposing a learning-augmented model-based planning approach that uses a deep convolutional neural network to estimate frontier properties, achieving coverage improvements of 2.1% over greedy strategies and 8.4% over RL-based methods on the Matterport3D dataset.
We consider the problem of time-limited robotic exploration in previously unseen environments where exploration is limited by a predefined amount of time. We propose a novel exploration approach using learning-augmented model-based planning. We generate a set of subgoals associated with frontiers on the current map and derive a Bellman Equation for exploration with these subgoals. Visual sensing and advances in semantic mapping of indoor scenes are exploited for training a deep convolutional neural network to estimate properties associated with each frontier: the expected unobserved area beyond the frontier and the expected timesteps (discretized actions) required to explore it. The proposed model-based planner is guaranteed to explore the whole scene if time permits. We thoroughly evaluate our approach on a large-scale pseudo-realistic indoor dataset (Matterport3D) with the Habitat simulator. We compare our approach with classical and more recent RL-based exploration methods. Our approach surpasses the greedy strategies by 2.1% and the RL-based exploration methods by 8.4% in terms of coverage.