论文标题
基于学习的基于模型的视觉探索计划
Learning-Augmented Model-Based Planning for Visual Exploration
论文作者
论文摘要
我们考虑在以前看不见的环境中探索受到预定时间的限制的时间限制机器人探索问题。我们使用基于学习的模型计划提出了一种新颖的探索方法。我们在当前地图上生成了与前沿相关的一组子目标，并得出了使用这些子目标进行探索的Bellman方程。视觉感应和室内场景的语义映射的进展被利用用于训练深度卷积神经网络，以估计与每个边界相关的特性：边界以外的预期未观察到的区域以及预期的时间段（离散的动作）来探索它。提出的基于模型的计划者可以保证，如果时间允许，可以探索整个场景。我们使用栖息地模拟器对大规模伪现实的室内数据集（Matterport3d）彻底评估我们的方法。我们将我们的方法与经典且基于RL的探索方法进行了比较。我们的方法超过了贪婪的策略2.1％，基于RL的探索方法在覆盖范围方面达到了8.4％。
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.
