论文标题
SEER：使用学习来预测信息增益的航空机器人的安全有效探索
SEER: Safe Efficient Exploration for Aerial Robots using Learning to Predict Information Gain
论文作者
论文摘要
我们解决了具有有限的感应功能和有效载荷/功率限制的微型航空车的高效3-D勘探问题。我们开发了一个室内探索框架，该框架使用学习来预测看不见的区域的占用，提取语义特征，示例观点以预测不同探索目标的信息收益，并计划提供信息轨迹，以实现安全和智能的探索。在模拟和实际环境中进行的广泛实验表明，就结构化室内环境的总路径长度而言，所提出的方法的表现优于最先进的勘探框架，并且在探索过程中的成功率更高。
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.
