Time-of-Flight camera based 3D point cloud reconstruction of a car

Modern car wash systems are generally controlled by light barriers and power measurement sensors. These sensors are directly mounted on the movable parts of the system which result in a slow regulation, leading to non-optimal performance, e.g. in terms of energy, water and detergent consumption or regarding the quality of the result. This paper presents an alternative approach, based on online automatic acquisition of 3D vehicle models, thus allowing for an a priori optimization of the washing process in order to achieve a better overall performance of the car wash system. Technically, the approach uses merged and synchronized data acquired by a multiple Time-of-Flight (ToF) camera setup. The proposed processing concept handles the range data sets acquired online, and performs data preprocessing, registration and fusion, as well as geometry extraction. The realized system is based on Graphics Processing Units (GPUs) in order to achieve a sufficient temporal processing performance. Furthermore, details are given showing how to solve the main challenges related to point cloud data processing, especially due to low image resolution, the influences of specific environment conditions, e.g. variations in coating and material reflectivity. The data registration is twofold, on the one hand the multiple ToF images are registered using the pre-calibrated camera system (extrinsic parameters), whereas the inter-frame registration is performed using a point-to-plane based iterative closest point (ICP) on the other hand. Finally, the merged data is integrated in a GPU data structure to allow a fast and efficient 3D point cloud reconstruction.