Eagleye: A Lane-Level Localization Using Low-Cost GNSS/IMU

Abstract

In this paper, we propose Eagleye, an open-source software, that performs lane level localization in an urban environment. A low-cost GNSS receiver, IMU, and velocity sensor are used for position estimation. The feature of this method is that it is optimized to take full advantage of the averaging effect using time series data longer than a few tens of seconds. This optimization improves the estimation performance by reducing the GNSS multipath in urban areas. In order to verify the effectiveness of the system, we conducted accuracy evaluation of the proposed method and performance comparison tests with expensive position estimation systems. As a result of the test, we confirmed that the proposed method can estimate the relative position results with an accuracy of 0.5 m per 100m and the absolute position performance with an accuracy of 1.5 m. In addition, it was confirmed that the performance of the proposed method was equivalent to that of an expensive system. Therefore, it is considered that the proposed method can effectively estimate the location even in an urban environment.