Detection of dynamic objects for environment mapping by time-of-flight cameras

We present a method for the detection of dynamic objects in the data of wide angle time-of-flight cameras. A vehicle is equipped with four cameras to incorporate a 360° vision by depth cameras. Depth measurements can be used for environment mapping. Dynamic objects however, lead to errors in the reconstruction of static scenes. Our method detects objects in the surrounding of the vehicle and discriminates between static and dynamic objects. Segments and keypoints are extracted from combinations of depth and amplitude images. After the generation and description of objects the vehicle odometry is incorporated for optimal object matching between consecutive images. Based on the computed movements of the objects a classification into static or dynamic is done. We evaluated our method based on different scenarios and under real world conditions including low lighting conditions and influence of external infrared light.