Object Reconstruction and Localization in Indoor Environments using Infrastructure Sensor Node

Indoor perception is a field that has gained traction in recent years. While there has been a significant amount of research done on outdoor perception and motion planning, the indoor environment has yet to receive similar treatment. In an indoor environment, various sensor systems have been developed to track and localize objects, each tackling a different set of challenges. In this paper, we introduce a novel Infrastructure Sensor Node (ISN) consisting of a LiDAR along with two monocular cameras mounted on the ceiling of the hallways of our lab to obtain relevant information. We present a perception pipeline that uses prior 3D point cloud registration to localize objects in real-time in cluttered indoor environments. We provided a complete case study to present a work that successfully detects, registers, and localizes objects through a cluttered environment with a high degree of occlusion.