Multiple hypothesis classification with laser range finders

An algorithm for tracking and classifying objects in urban areas using a multi-layer laser range finder is presented. Due to object disintegration caused by occlusions and fine segmentation of the data in order to separate close objects, classification is not always simple. Therefore, a multiple hypothesis approach is proposed, which keeps track of all feasible combinations of segments. The algorithm takes all segment combinations to create hypotheses and these are tracked over time using a Kalman filter. Due to the large number of hypotheses, restrictions are applied to reduce the number of hypotheses. Since applications need a description of the environment, which is described by objects, hypotheses are selected by their qualities and are provided as objects.