Department of Mechanical Engineering, University of Aveiro, 3810-193 Aveiro, Portugal.
Institute of Electronics and Informatics Engineering of Aveiro, 3810-193 Aveiro, Portugal.
Sensors (Basel). 2020 Aug 4;20(15):4331. doi: 10.3390/s20154331.
Carrying out the task of the exploration of a scene by an autonomous robot entails a set of complex skills, such as the ability to create and update a representation of the scene, the knowledge of the regions of the scene which are yet unexplored, the ability to estimate the most efficient point of view from the perspective of an explorer agent and, finally, the ability to physically move the system to the selected Next Best View (NBV). This paper proposes an autonomous exploration system that makes use of a dual OcTree representation to encode the regions in the scene which are occupied, free, and unknown. The NBV is estimated through a discrete approach that samples and evaluates a set of view hypotheses that are created by a conditioned random process which ensures that the views have some chance of adding novel information to the scene. The algorithm uses ray-casting defined according to the characteristics of the RGB-D sensor, and a mechanism that sorts the voxels to be tested in a way that considerably speeds up the assessment. The sampled view that is estimated to provide the largest amount of novel information is selected, and the system moves to that location, where a new exploration step begins. The exploration session is terminated when there are no more unknown regions in the scene or when those that exist cannot be observed by the system. The experimental setup consisted of a robotic manipulator with an RGB-D sensor assembled on its end-effector, all managed by a Robot Operating System (ROS) based architecture. The manipulator provides movement, while the sensor collects information about the scene. Experimental results span over three test scenarios designed to evaluate the performance of the proposed system. In particular, the exploration performance of the proposed system is compared against that of human subjects. Results show that the proposed approach is able to carry out the exploration of a scene, even when it starts from scratch, building up knowledge as the exploration progresses. Furthermore, in these experiments, the system was able to complete the exploration of the scene in less time when compared to human subjects.
自主机器人执行场景探索任务需要一系列复杂的技能，例如创建和更新场景表示的能力、了解场景中尚未探索的区域、从探索代理的角度估计最有效视点的能力，以及最终将系统物理移动到所选的最佳下一个视点 (NBV) 的能力。本文提出了一种自主探索系统，该系统利用双 OcTree 表示法来对场景中被占据、自由和未知的区域进行编码。NBV 通过离散方法进行估计，该方法对一组由条件随机过程创建的视图假设进行采样和评估，该过程确保视图有一定的机会为场景添加新信息。该算法使用根据 RGB-D 传感器特性定义的光线投射，以及一种机制对要测试的体素进行排序，这大大加快了评估速度。选择估计提供最大信息量的采样视图，并将系统移动到该位置，在该位置开始新的探索步骤。当场景中不再存在未知区域或者系统无法观察到这些区域时，探索会话结束。实验设置包括一个带有 RGB-D 传感器的机器人操纵器，该传感器安装在其末端执行器上，所有这些都由基于机器人操作系统 (ROS) 的架构管理。操纵器提供运动，而传感器收集有关场景的信息。实验结果跨越了三个测试场景，旨在评估所提出系统的性能。特别是，将提出的系统的探索性能与人类受试者的性能进行了比较。结果表明，该方法能够执行场景的探索，即使从一开始就没有任何知识，随着探索的进行，它会逐渐建立知识。此外，在这些实验中，与人类受试者相比，系统能够在更短的时间内完成场景的探索。
