GNSS測位の高度化と自動運転への応用
【研究分野】知覚情報処理
【研究キーワード】
GPS / GNSS / Multipath / Urban canyon / Localization / 3D Map / 3D maps / 3D building model / Urban Canyon / RAIM / Autonomous driving
【研究成果の概要】
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.
【研究代表者】
【研究種目】特別研究員奨励費
【研究期間】2015-04-24 - 2017-03-31
【配分額】2,300千円 (直接経費: 2,300千円)