使用Marvelmind进行3D定位和数据质量评估

Máté Vágner, Dénes Palkovics, László Kovács
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引用次数: 1

摘要

定位和位置估计是自动驾驶中的关键任务。除了定位的重要性之外,有了高质量的位置跟踪,就可以实施复杂的数据收集程序,并使用强化学习等先进的机器学习方法。全球导航卫星系统提供非常精确的定位,但在某些实验室条件下,它们的使用是繁琐的或不可能的。在我们的工作中,我们应用了一个室内定位系统,它被集成到我们的1:16自动驾驶汽车中。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
3D Localization and Data Quality Estimation with Marvelmind
Localization and position estimation are crucial tasks in autonomous driving. In addition to the importance of positioning, with good quality position tracking, it is possible to implement sophisticated data collection procedures and use advanced machine learning methods such as reinforced learning. Global navigation satellite systems offer very accurate positioning, but their use is cumbersome or impossible under certain laboratory conditions. In our work, we applied an indoor positioning system, that is integrated into our 1:16 self-driving car.
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