  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.
