Detection of moving objects from point cloud data using photometric consistency and prior knowledge of road environment

3D models of urban environments are constructed based on measured data using several types of sensors such as laser rangefinders, cameras, GPS sensors and gyroscopes. In this 3D modeling process, 3D points on moving objects become obstacles to enable the construction of an accurate 3D model. To solve this problem, this paper has proposed a method for detecting and removing 3D points on moving objects from 3D point cloud data based on photometric consistency and knowledge of the road environment. In our method, 3D points on moving objects are detected based on luminance variations computed by projecting 3D points onto omnidirectional images. After detecting candidates for points on moving objects, points inside a certain size of region determined by prior knowledge of road environment are removed. We show the effectiveness of the method through experiments for a real outdoor environment.