Abstract
One essential step to realize modern driver assistance technology is the accurate knowledge about the location of static objects in the environment. In this work, we use artificial neural networks to predict the occupation state of a whole scene in an end-to-end manner. This stands in contrast to the traditional approach of accumulating each detection's influence on the occupancy state and allows to learn spatial priors which can be used to interpolate the environment's occupancy state. We show that these priors make our method suitable to predict dense occupancy estimations from sparse, highly uncertain inputs, as given by automotive radars, even for complex urban scenarios. Furthermore, we demonstrate that these estimations can be used for large-scale mapping applications.
Abstract (translated)
实现现代驾驶辅助技术的一个重要步骤是准确了解静态物体在环境中的位置。在这项工作中，我们使用人工神经网络以端到端的方式预测整个场景的占用状态。这与传统的积累每个检测对占用状态的影响的方法形成了对比，并允许学习空间先验，这些先验可用于插入环境的占用状态。我们表明，这些先验使我们的方法适用于预测密集占用估计稀疏，高度不确定的输入，如汽车雷达给出的，甚至复杂的城市场景。此外，我们证明了这些估计可用于大规模的映射应用。
URL
https://arxiv.org/abs/1903.12467
