LaneMatch: A Practical Real-Time Localization Method Via Lane-Matching

This letter presentsLaneMatch,a localization method for use in autonomous vehicles (AVs). It utilizes lane matching to obtain an AV’s lane occupancy and current pose estimation. Matching is performed on a compact low-resolution road map generated from satellite images. Our approach addresses the following challenges for AVs using this map: (1) misalignment between roads on the satellite images and their global coordinates, and (2) incomplete or incorrect lane detection outputs. First,LaneMatchestimates the offset between the AV’s global pose in the global coordinate system and its local map pose on the map. Secondly,LaneMatchutilizes a spatio-temporal integration of a particle filter and a factor graph to resolve lane-matching ambiguities. It strategically constrains the dimensionality of variables to obtain real-time performance. We use highway experiments to evaluate the processing time, occupancy accuracy, lateral/longitudinal and position/heading errors. These experiments show thatLaneMatchlocalizes our AV rather precisely on the road map in real-time and can be used for navigation and planning purposes even in GNSS-unfriendly areas.