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Linear-chain CRF based intersection recognition | IEEE Conference Publication | IEEE Xplore

Linear-chain CRF based intersection recognition


Abstract:

For autonomous navigation in urban environments, the ability to detect road intersections in advance is crucial, especially in the absence of auxiliary geographic informa...Show More

Abstract:

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.
Date of Conference: 16-17 December 2014
Date Added to IEEE Xplore: 23 March 2015
Electronic ISBN:978-1-4799-1882-9
Conference Location: Hyderabad, India

References

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