Human Recognition and Tracking in Narrow Indoor Environment using 3D Lidar Sensor

This paper studies the human recognition, tracking, and clustering method in an indoor environment using a 3D lidar sensor and discusses two major issues in clustering. The first problem is when the Euclidean distance-based clustering is used, where a wall and a person are frequently clustered into one object. The other issue is that there is some noise due to reflective materials such as glass or marble. In order to cluster objects and recognize humans in this environment, we proposed a pre-processing sequence module for clustering. The pre-processing module composed in 5 steps that can remove walls around the robot and reduce the point cloud noise. We embedded this whole process in the robot system and it works while the robot is in motion.