Search Results

Aimed to provide an effective solution for control-oriented applications, this paper proposes a novel method using a high-precision digital map to achieve high-accuracy positioning with fast updating rate. First, the map is developed using a high-definition LiDAR (Velodyne HDL 64E) and a RTK-GNSS system, which contains lane-level waypoints, road width, curb and typical obstacles along the road. Next, a robust version of ICP (Iterative Closest Point) is proposed to clean the corresponding points of large errors on map matching (MM). Finally, based on the large set of data from the environmental map, an unscented Kalman filter (UKF) is applied to fuse GNSS signal and dead reckoning (DR) to estimate the position. Thus the searching scope on the map can be considerably reduced so that the matching speed can be greatly improved. The high-precision digital map can be used not only for global path planning, but also for local driving detection and path planning.

Intelligent vehicles have gained increasing popularity in recent years as traffic safety and efficiency have become the major challenges faced by automotive industry. Vehicle positioning system, such as GPS, plays more and more important role on intelligent or autonomous driving. Intelligent vehicle technologies have been developed and tested mainly based on intensive field experiment under various driving scenarios. However, the large variation, uncertainty and complexity of the driving environment, including buildings, traffic and weather conditions have posed great challenges on test repeatability and system robustness. This paper proposes a GPS model considering software-centered observation errors. The focus of the research is on its error to reflect the real signals from GPS measurement.