Road-Pulse From IMU to Enhance HD Map Matching for Intelligent Vehicle Localization

Matching high definition (HD) map and real-time sensor data, which is crucial for map-based intelligent vehicle localization, usually requires robust and distinct road features extracted from both ends. However, road features extacted from sensor data (e.g., image, LiDAR) are susceptible to various conditions, such as lighting, weather conditions, dynamic objects, etc. Moreover, although lane lines can provide continuous lateral constraints on a vehicle's position, it is still challenging to get sufficient longitudinal ones due to the limited road markings. In this article, we employed the longitudinal distance provided by road-pulse, which is the vibration pattern while the vehicle passes lateral road elements (LREs) such as speed bumps, expansion joints, etc., to enhance the localization accuracy. Such road-pulse vibration patterns can be detected accurately with the proposed Sliding Window Gaussian Model (SWGM) from the acceleration data collected using a low-cost inertial measurement unit (IMU). Detection of road-pulse allows accurate and robust matching of LREs within the HD map to provide accurate longitudinal distances. Then the longitudinal and the lateral distance(s) derived from IMU and off-the-shelf around view monitoring (AVM) are mapped into global constraints with the support of the HD map. Finally, these constraints, together with the measurement from the consumer-level inertial navigation system (INS), are fused within a framework of Kalman Filter based on the Markov model (KF-MM) to achieve accurate vehicle localization. The proposed method has been verified on two routes with a total length of 3.7 km. Experimental results demonstrate that road-pulse can significantly enhance the localization results with good robustness to different scenarios. After fusing with road-pulses, the overall localization error can achieve an average decimeter accuracy on both test routes.