LiDAR- and Radar-Based Robust Vehicle Localization with Confidence Estimation of Matching Results
Ryo Yanase, Daichi Hirano, Mohammad Aldibaja, Keisuke Yoneda, Naoki Suganuma
Abstract
Localization is an important technology for autonomous driving. Map-matching using road surface pattern features gives accurate position estimation and has been used in autonomous driving tests on public roads. To provide highly safe autonomous driving, localization technology that is not affected by the environment is required. In particular, in snowy environments, the features of the road surface pattern may not be used for matching because the road surface is hidden. In such cases, it is necessary to construct a robust system by rejecting the matching results or making up for them with other sensors. On the other hand, millimeter-wave radar-based localization methods are not as accurate as LiDAR-based methods due to their ranging accuracy, but it has successfully achieved autonomous driving in snowy environments. Therefore, this paper proposes a localization method that combines LiDAR and millimeter-wave radar. We constructed a system that emphasizes LiDAR-based matching results during normal conditions when the road surface pattern is visible and emphasizes radar matching results when the road surface is not visible due to snow cover or other factors. This method achieves an accuracy that allows autonomous driving to continue regardless of normal or snowy conditions and more robust position estimation.