Context Extraction from GIS Data Using LiDAR and Camera Features

We propose a method to extract spatial context of unknown objects in a driving scenario by classifying the surfaces in which the traffic participants transit. In order to classify these surfaces without the need for a big amount of labeled data, we resort to an unsupervised learning method that clusters patches of terrain using features extracted from LiDAR and image data. Using an iterative method, we are able to model the characteristics of map features from a geographical information system (GIS), such as streets and sidewalks, and extend their contextual meaning to the area around our test vehicle. We evaluate our results using a partially labeled 3D scan of our campus and find that our method is able to correctly extract and extend the spatial context of the map features from the GIS to the labeled surfaces on the campus.