HD Map Errors Detection using Smoothing and Multiple Drives

Abstract

High Definition (HD) maps enable autonomous vehicles to not only navigate roads but also localize. Using perception sensors such as cameras or lidars, map features can be detected and used for localization. The accuracy of vehicle localization is directly influenced by the accuracy of the features. It is therefore essential for the localization system to be able to detect erroneous map features. In this paper, an approach using Kalman smoothing with observation residuals is presented to address this issue. A covariance intersection of the residuals is proposed to manage their unknown correlation. The method also leverages the information of multiple runs to improve the detection of small errors. The performance of the method is evaluated using experimental data recorded on public roads with erroneous road signs. Our results allow to evaluate the gain of detection brought during successive drives.