Searching for objects is a fundamental skill for robots. As such, we expect object search to eventually become an off-the-shelf capability for robots, similar to e.g., object detection and SLAM. In contrast, however, no system for 3D object search exists that generalizes across real robots and environments. In this paper, building upon a recent theoretical framework that exploited the octree structure for representing belief in 3D, we present GenMOS (Generalized Multi-Object Search), the first general-purpose system for multi-object search (MOS) in a 3D region that is robot-independent and environment-agnostic. GenMOS takes as input point cloud observations of the local region, object detection results, and localization of the robot's view pose, and outputs a 6D viewpoint to move to through online planning. In particular, GenMOS uses point cloud observations in three ways: (1) to simulate occlusion; (2) to inform occupancy and initialize octree belief; and (3) to sample a belief-dependent graph of view positions that avoid obstacles. We evaluate our system both in simulation and on two real robot platforms. Our system enables, for example, a Boston Dynamics Spot robot to find a toy cat hidden underneath a couch in under one minute. We further integrate 3D local search with 2D global search to handle larger areas, demonstrating the resulting system in a 25m$^2$ lobby area.

翻译：搜索目标是机器人的一项基础技能。因此，我们期望目标搜索最终能成为机器人的现成能力，类似于目标检测和SLAM。然而，目前尚不存在能够跨真实机器人和环境泛化的三维目标搜索系统。本文基于近期利用八叉树结构表示三维信度的理论框架，提出GenMOS（通用多目标搜索）——首个在三维区域中实现机器人无关、环境无关的通用多目标搜索系统。GenMOS以局部区域点云观测、目标检测结果和机器人视角位姿定位为输入，通过在线规划输出6自由度视角运动方向。具体而言，GenMOS以三种方式利用点云观测：（1）模拟遮挡；（2）推断占据状态并初始化八叉树信度；（3）采样障碍物避免的信度依赖视角位置图。我们在仿真环境和两种真实机器人平台上评估了该系统。例如，该系统使Boston Dynamics Spot机器人在一分钟内找到隐藏在沙发下的玩具猫。我们进一步将三维局部搜索与二维全局搜索集成以处理更大区域，并在25平方米的大厅区域展示了最终系统的性能。