People Detection in 3d Point Clouds Using Local Surface Normals

The ability to detect people in domestic and unconstrained environments is crucial for every service robot. The knowledge where people are is required to perform several tasks such as navigation with dynamic obstacle avoidance and human-robot-interaction. In this paper we propose a people detection approach based on 3d data provided by a RGB-D camera. We introduce a novel 3d feature descriptor based on Local Surface Normals (LSN) which is used to learn a classifier in a supervised machine learning manner. In order to increase the systems flexibility and to detect people even under partial occlusion we introduce a top-down/bottom-up segmentation. We deployed the people detection system on a real-world service robot operating at a reasonable frame rate of 5Hz. The experimental results show that our approach is able to detect persons in various poses and motions such as sitting, walking, and running.