High-precision Positioning using Height-constraint RTK Method under Urban Environments

Abstract

High-precision positioning methods have drawn great attention in recent years due to the rapid development of intelligent vehicles and automatics driving. Real Time Kinematic (RTK) is a classical technology to achieve centimeter accuracy in open sky areas. However, vehicles driving under urban scenarios are always confronted with harsh Global Navigation Satellite System (GNSS) environment. Surrounding obstacles like buildings or overpasses may cause signal blockages or multipath noise, making it difficult to achieve continuous precise positions with a high ambiguity fixing rate. Even in harsh urban environment, the height of road is relatively stable and could be served as a piece of useful information, but few studies use height information for positioning improvement. In this paper, we propose a height-constraint RTK method to improve the positioning performance cooperatively. In a vehicle-to-vehicle (V2V) network, the position information of cooperative vehicles is used to construct a height plane for constraining the positioning results of vehicles. To validate the performance of our proposed method, a field test was carried out in Be?ing with a four-vehicle fleet. Under urban environment, vehicles only reach 53% ambiguity fixed rate with traditional RTK method in average, but our proposed method can achieve a fixed rate of 68%. The root mean square error (RMSE) of traditional RTK method is 1.5729m, but only 0.72m for our proposed method. Results show that the proposed method takes advantage of the height information and thus obtains a much higher fixed rate and more accurate positioning results under urban scenarios.