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Detecting objects of interest, such as human survivors, safety equipment, andstructure access points, is critical to any search-and-rescue operation. Robotsdeployed for such time-sensitive efforts rely on their onboard sensors toperform their designated tasks. However, as disaster response operations arepredominantly conducted under perceptually degraded conditions, commonlyutilized sensors such as visual cameras and LiDARs suffer in terms ofperformance degradation. In response, this work presents a method that utilizesthe complementary nature of vision and depth sensors to leverage multi-modalinformation to aid object detection at longer distances. In particular, depthand intensity values from sparse LiDAR returns are used to generate proposalsfor objects present in the environment. These proposals are then utilized by aPan-Tilt-Zoom (PTZ) camera system to perform a directed search by adjusting itspose and zoom level for performing object detection and classification indifficult environments. The proposed work has been thoroughly verified using anANYmal quadruped robot in underground settings and on datasets collected duringthe DARPA Subterranean Challenge finals.

LiDAR-guided object search and detection in Subterranean Environments