We address the problem of efficient 3-D exploration in indoor environments for micro aerial vehicles with limited sensing capabilities and payload/power constraints. We develop an indoor exploration framework that uses learning to predict the occupancy of unseen areas, extracts semantic features, samples viewpoints to predict information gains for different exploration goals, and plans informative trajectories to enable safe and smart exploration. Extensive experimentation in simulated and real-world environments shows the proposed approach outperforms the state-of-the-art exploration framework by 24% in terms of the total path length in a structured indoor environment and with a higher success rate during exploration.

翻译：针对具备有限感知能力及载荷/功率约束的微型飞行器室内三维高效探索问题，本文提出一种室内探索框架。该框架通过预测未观测区域占用状态、提取语义特征、采样视点以预测不同探索目标的信息增益，并规划信息量丰富的轨迹，实现安全智能的探索行为。在仿真与真实环境中的大量实验表明，在结构化室内环境中，本方法相较于现有最优探索框架，在总路径长度上降低24%，且探索成功率更高。