Linear-chain CRF based intersection recognition

For autonomous navigation in urban environments, the ability to detect road intersections in advance is crucial, especially in the absence of auxiliary geographic information. In this paper we investigate a 3D Point Cloud based solution for intersection recognition and road segment classification. We set up the intersection recognition problem as one of decoding a linear-chain Conditional Random Field (CRF). This allows us to encode temporal consistency relations between adjacent scans in our process, leading to a less error prone recognition algorithm. We quantify this claim experimentally. We first build a grid map of the point cloud, segmenting the region surrounding the robot into navigable and non-navigable regions. Then, based on our proposed beam model, we extract a descriptor of the scene. This we do as each scan is received from the robot. Based on the descriptor we build a linear chain-CRF. By decoding the CRF-chain we are able to recognize the type of road segment taken into consideration. With the proposed method, we are able to recognize Xjunctions, T-shaped intersections and standard non-branching road segments. We compare the CRF-based approach with a standard SVM based one and show performance gain due to the CRF formulation.