| 研究実績の概要 |
Vehicle self-localization in urban environment is a challenging but significant topic for autonomous driving and driving assistance. Both motion planning and vehicle cooperation need the accurate position information. This research focused on both passive sensor-based and active sensor-based vehicle self-localization systems.
At the beginning of this research project, we firstly proposed to integrate 3D map based GNSS with other passive sensors: Inertial Measurement Unit (IMU), vehicle speedometer and an onboard camera. We conducted a series of tests in different places of Tokyo city. The experiment results demonstrate that the proposed passive sensor-based localization system can achieve sub-meter accuracy with respect to positioning error mean. After that, we focused on active sensor-based vehicle self-localization with the abstract map. Experiments conducted in one of the urban areas of Tokyo show that even though we extremely shrank the map size, we could preserve the mean error of the localization about 50 centimeters. In the past year, we extend our research to smartphone based pedestrian positioning and navigation system. Research result indicated that using map and context information can improve pedestrian positioning accuracy in the city urban environment.
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