The paper proposes a reliable and robust planning solution to the long range robotic navigation problem in extremely cluttered environments. A two-layer planning architecture is proposed that leverages both the environment map and the direct depth sensor information to ensure maximal information gain out of the onboard sensors. A frontier-based pose sampling technique is used with a fast marching cost-to-go calculation to select a goal pose and plan a path to maximize robot exploration rate. An artificial potential function approach, relying on direct depth measurements, enables the robot to follow the path while simultaneously avoiding small scene obstacles that are not captured in the map due to mapping and localization uncertainties. We demonstrate the feasibility and robustness of the proposed approach through field deployments in a structurally complex warehouse using a micro-aerial vehicle (MAV) with all the sensing and computations performed onboard.

中文翻译：

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非结构化环境的 3D 反应控制和基于前沿的探索

该论文针对极其杂乱环境中的远程机器人导航问题提出了一种可靠且稳健的规划解决方案。提出了一种两层规划架构，它利用环境地图和直接深度传感器信息来确保从板载传感器中获得最大信息。基于前沿的姿势采样技术与快速行进成本计算一起使用，以选择目标姿势并规划路径以最大化机器人探索率。依赖于直接深度测量的人工势函数方法使机器人能够跟随路径，同时避免由于映射和定位不确定性而未在地图中捕获的小场景障碍物。