Abstract
In this letter, we propose a method for accurately estimating the 6-Degree Of Freedom (DOF) pose in an urban environment when a High Definition (HD) map is available. An HD map expresses 3D geometric data with semantic information in a compressed format and thus is more memory-efficient than point cloud maps. The small capacity of HD maps can be a significant advantage for autonomous vehicles in terms of map storage and updates within a large urban area. Unfortunately, existing approaches failed to sufficiently exploit HD maps by only estimating partial pose. In this study, we present a full 6-DOF localization against an HD map using an onboard stereo camera with semantic information from roads. We introduce an 8-bit representation for road information, which allow for effective bitwise operation when matching between query data and the HD map. For the pose estimation, we leverage a particle filter followed by a full 6-DOF pose optimization. Our experimental results show a median error of approximately 0.3 m in the lateral and longitudinal directions for a drive of approximately 11 km. These results can be used by autonomous vehicles to correct the global position without using Global Positioning System (GPS) data in highly complex urban environments. The median operation speed is approximately 60 msec supporting 10 Hz.
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