ＧＮＳＳ測位の高度化と自動運転への応用

精确定位是实现城市区域自动驾驶的关键。一般的理解是将GNSS接收机、激光雷达和视觉传感器集成在一起，提供车道级精度的定位服务。一般来说，车道的宽度在3米左右。根据最近的研究，GNSS、LiDAR和相机的定位方法可以通过应用2D地图和3D模型来改进。自2007年以来，使用3D建筑模型作为辅助工具来减轻/排除/纠正多路径和非视距效应已成为增强GNSS接收器的流行方法。提出了一种利用三维建筑模型和光线追踪模拟的GPS误差校正方法。最后，该方法成功地实现了定位误差小于3米的性能。

Precise positioning is essential to realize autonomous driving in city urban area. The common understanding is GNSS receiver, LiDAR and vision sensor are integrated to provide localization service with lane-level accuracy. In general, the width of lane is about 3 meters. According to recent studies, localization methods of GNSS, LiDAR and camera can be improved by applying 2D maps and 3D models. Since 2007, using 3D building models as aids to mitigate/exclude/correct the multipath and non-line-of-sight effects has become a popular approach to enhance GNSS receivers. We propose an innovative GPS error correction method by the use of 3D building model and ray-tracing simulation. Finally, the proposed method successfully achieves a performance with less than 3 meters of positioning error.