Research on map matching of lidar/vision sensor for automatic driving aided positioning
by Qing An; Xijiang Chen; Yuhua OuYang
International Journal of Vehicle Information and Communication Systems (IJVICS), Vol. 6, No. 2, 2021

Abstract: Aiming at the technical difficulties of map matching aided positioning based on lidar/vision sensor, the joint calibration of lidar/vision sensor and point cloud/image registration technology, as well as the dynamic environment interference removal method based on depth learning are studied. In this paper, a lightweight coding-decoding architecture is introduced. We use deep separable convolution technology to extract urban environment features and generate semantic-level feature descriptors. The similarity measurement criteria that contain semantic information and geometric state are established. Then, we perform the robust feature matching. Finally, a map matching location model based on recursive Bayesian filtering optimisation framework and an estimation method of location confidence are proposed. It realises the map assistant positioning under the complex environment of the city. In typical urban environments, the speed of feature extraction and matching is better than 0.1s, the success rate of matching is more than 95%, and the positioning accuracy of high-precision map matching is better than 20 cm.

Online publication date: Wed, 26-May-2021

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