Visual Semantic Localization based on HD Map for Autonomous Vehicles in Urban Scenarios

Highly accurate and robust localization ability is of great importance for autonomous vehicles (AVs) in urban scenarios. Traditional vision-based methods suffer from lost due to illumination, weather, viewing and appearance changes. In this paper we propose a novel visual semantic localization algorithm based on HD map and semantic features which are compact in representation. Semantic features are widely appeared on urban roads, and are robust to illumination, weather, viewing and appearance changes. The repeated structures, missed detections and false detections make data association (DA) highly ambiguous. To this end, a robust DA method considering local structural consistency, global pattern consistency and temporal consistency is performed. Further, we introduce a sliding window factor graph optimization framework to fuse association and odometry measurements without the requirements of high-precision absolute height information for map features. We evaluate the proposed localization framework on both simulated and real urban road. The experiments show that the proposed approach is able to achieve highly accurate localization with a mean longitudinal error of 0.43m, a mean lateral error of 0.12m and a mean yaw angle error of 0.11 degrees.