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Evaluation of automatically extracted landmarks for future driver assistance systems

机译:评估为将来的驾驶员辅助系统自动提取的地标

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摘要

In the future, vehicles will gather more and more spatial information about their environment, using on-board sensors such as cameras and laser scanners. Using this data, e.g. for localization, requires highly accurate maps with a higher level of detail than provided by today's maps. Producing those maps can only be realized economically if the information is obtained fully automatically. It is our goal to investigate the creation of intermediate level maps containing geo-referenced landmarks, which are suitable for the specific purpose of localization. To evaluate this approach, we acquired a dense laser scan of a 22 km scene, using a mobile mapping system. From this scan, we automatically extracted pole-like structures, such as street and traffic lights, which form our pole database. To assess the accuracy, ground truth was obtained for a selected inner-city junction by a terrestrial survey. In order to evaluate the usefulness of this database for localization purposes, we obtained a second scan, using a robotic vehicle equipped with an automotive-grade laser scanner. We extracted poles from this scan as well and employed a local pole matching algorithm to improve the vehicle's position.
机译:将来,车辆将使用摄像头和激光扫描仪等车载传感器来收集有关其环境的越来越多的空间信息。使用此数据,例如为了进行本地化,需要高度精确的地图,其详细程度要比当今的地图更高。只有完全自动获得信息,才能经济地实现这些地图的制作。我们的目标是研究包含特定于地理定位的地标的中级地图的创建。为了评估这种方法,我们使用移动制图系统对22 km的场景进行了密集的激光扫描。从此扫描中,我们自动提取了类似杆的结构,例如路灯和交通灯,这些形成了杆数据库。为了评估准确性,通过地面调查获得了选定的市区交界处的地面真相。为了评估该数据库对本地化的有用性,我们使用配备了汽车级激光扫描仪的机器人车辆进行了第二次扫描。我们也从这次扫描中提取了极点,并采用了局部极点匹配算法来改善车辆的位置。

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