Layer-based Supervised Classification of Moving Objects in Outdoor Dynamic Environment using 3D Laser Scanner

In this paper, we present a layered approach for classification of moving objects from 3D range data based on supervised learning technique. Our approach combines the model based classification in 2D with boosting for classifying the objects into four classes of interest namely bus, car, bike and pedestrian. In contrast to most of the existing work on 3D classification which involves extensive feature extraction and description, this combination uses simple single-valued features and allows our system to perform efficiently. The proposed method can be used in conjunction with any type of range sensors, however, we have demonstrated its performance using the data acquired from a Velodyne HDL-64E laser scanner.