A new geometric 3D LiDAR feature for model creation and classification of moving objects

In this paper, we introduce a new geometric 3D feature combined with a clustering approach. Besides 3D data provided by a LiDAR point cloud, reflectivity information is used to further enhance the descriptivity of the feature. The proposed feature can be extracted and compared in real-time. Similar parts of an object, such as features belonging to an automobile headlight, are automatically clustered in an object model without explicit specification. Additionally, we provide a method for autonomous vehicles to automatically learn the shapes of observed moving objects and use them for real-time classification. The resulting object models consisting of the extracted feature clusters are interpretable by humans.