As unmanned ground vehicles (UGVs) applications expand to large-scale and open road scenarios, vectorized highdefinition maps (VHD maps) demonstrate greater potential in solving state correction problems than traditional metric maps. Previous related studies typically employ proprietary versions of VHD maps without fully leveraging the common traffic elements’ information. In addition, there is a lack of research on efficient interaction methods between UGVs and VHD maps. To fill these gaps, we propose a universal UGV state estimation system and a query-based VHD map data exchange protocol. The system utilizes VHD maps to correct the lateral and longitudinal positions as well as the yaw orientation of UGVs. The data exchange protocol enables UGVs to obtain real-time VHD map information and process it efficiently. To ensure universality, we accommodate two widely used VHD map formats, ASAM OpenDRIVE and Apollo OpenDRIVE and provide corresponding map parsing methods. The evaluation of the system is conducted both in simulated and real-world scenes. In the simulation experiments, we fully measure the effectiveness and accuracy of our method, as well as its sensitivity to measurement noise. In real-world experiments, we compare the state estimation accuracy of our system with SOTA simultaneous localization and mapping methods on an open road. The results show that our system demonstrates better accuracy than other baselines on most data sequences. The proposed map data exchange protocol meets real-time requirements.

System Architecture

Supplemental Videos

The supplemental videos are mainly work demos of various state correction modes.

The first two videos are demos of position correction based on VHD map reference. The video on the left uses the longitudinal constraint target object as the correction source, while the video on the right uses the lane line (lateral constraint target object) as the correction source.

The following two videos are demos of UGV position correction using GNSS as a reference source. Thanks to the mechanism of variance redundancy calculation, our system can robustly correct the position of UGV in the presence of noise interference in GNSS signals.

The following video demonstrates how our system recognizes longitudinal constrained targets represented by pedestrian crossings.