Object identification by 3D LIDAR using nested infinite Gaussian mixture model

For an autonomous mobile robot that works in real-world environment, recognition of its surrounding environment is necessary. This paper presents an object identification system, which can identify known and unknown objects and estimate their locations using 3D point cloud data acquired from a 3D LIDAR sensor mounted on the mobile robot. The proposed system is divided into two main steps; the first step is segmentation based on the 3D point cloud and the second step is identification of the extracted objects. The 3D LIDAR sensor gives sparse 3D shape information accurately, and covers a wide range. One of the main problems of such data is that the object shape information varies according to object's orientation and distance from the sensor. To solve this problem, we use nested infinite Gaussian mixture models for object identification. The experimental results show that the proposed system can extract various types of objects and identify both known and unknown objects.