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LiDAR-Aided Visual-Inertial Localization with Semantic Maps

Accurate and robust localization is an essential task for autonomous driving systems. In this paper, we propose a novel 3D LiDAR-aided visual-inertial localization method. Our method fully explores the complementarity of visual and LiDAR observations. On the one hand, the association between semantic features in images and a given semantic map provides constraints for the absolute pose. On the other hand, LiDAR odometry (LO) can provide an accurate and robust 6DOF relative pose. The Error State Kalman Filter (ESKF) framework is exploited to estimate the vehicle pose relative to the semantic map, which fuses the global constraints between the image and the semantic map, the relative pose from the LO, and the raw IMU data. The method achieves centimeter-level localization accuracy in a variety of challenging scenarios. We validate the robustness and accuracy of our method in real-world scenes over 50 km. The experimental results show that the proposed method is able to achieve an average lateral accuracy of 0.059 m and longitudinal accuracy of 0.158 m, which demonstrates the practicality of the proposed system in autonomous driving applications.