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:我们解决了在室内环境中对遥感能力有限和有效载荷/功率有限的微型飞行器进行高效三维探索的问题,我们开发了一个室内探索框架,利用学习来预测隐蔽区域的占用情况,提取语义特征,样本观点来预测不同勘探目标的信息收益,并规划信息丰富的轨迹以进行安全和智能的探索。 在模拟和现实世界环境中进行的广泛实验表明,拟议的方法在结构化室内环境的总路径长度方面比最先进的探索框架高出24%,在勘探期间的成功率更高。</s>