论文标题
LIDAR引导的对象搜索和地下环境中的检测
LiDAR-guided object search and detection in Subterranean Environments
论文作者
论文摘要
检测感兴趣的对象，例如人类幸存者，安全设备和结构接入点，对于任何搜索操作至关重要。为时间敏感的工作而部署的机器人依靠他们的车载传感器来执行其指定任务。但是，由于灾难响应操作主要是在感知降低的条件下进行的，因此通常使用的传感器（例如相机和LIDARS）在性能降解方面受到了影响。作为回应，这项工作提出了一种利用视觉和深度传感器的互补性质来利用多模式信息来帮助对象检测的方法。特别是，使用稀疏激光雷达回报的深度和强度值用于生成环境中存在的对象的建议。然后，这些建议是通过Pan-Tilt-Zoom（PTZ）摄像头系统使用的，通过调整其姿势和缩放级别来执行定向搜索，以在困难的环境中执行对象检测和分类。拟议的工作已在地下环境和DARPA Subterranean挑战决赛期间收集的数据集中进行了彻底验证。
Detecting objects of interest, such as human survivors, safety equipment, and structure access points, is critical to any search-and-rescue operation. Robots deployed for such time-sensitive efforts rely on their onboard sensors to perform their designated tasks. However, as disaster response operations are predominantly conducted under perceptually degraded conditions, commonly utilized sensors such as visual cameras and LiDARs suffer in terms of performance degradation. In response, this work presents a method that utilizes the complementary nature of vision and depth sensors to leverage multi-modal information to aid object detection at longer distances. In particular, depth and intensity values from sparse LiDAR returns are used to generate proposals for objects present in the environment. These proposals are then utilized by a Pan-Tilt-Zoom (PTZ) camera system to perform a directed search by adjusting its pose and zoom level for performing object detection and classification in difficult environments. The proposed work has been thoroughly verified using an ANYmal quadruped robot in underground settings and on datasets collected during the DARPA Subterranean Challenge finals.
