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：我们针对具有有限感知能力及负载/功率限制的微型飞行器在室内环境中的高效三维探索问题展开研究。提出了一种室内探索框架，该框架利用学习技术预测未知区域的占用状态，提取语义特征，采样视点以预测不同探索目标的信息增益，并规划信息丰富的轨迹以实现安全智能的探索。在仿真与真实环境中的大量实验表明，所提方法在结构化室内环境中的总路径长度方面较现有最优探索框架提升24%，且探索过程中的成功率更高。