Evidential occupancy grid maps (OGMs) are a popular representation of the environment of automated vehicles. Inverse sensor models (ISMs) are used to compute OGMs from sensor data such as lidar point clouds. Geometric ISMs show a limited performance when estimating states in unobserved but inferable areas and have difficulties dealing with ambiguous input. Deep learning-based ISMs face the challenge of limited training data and they often cannot handle uncertainty quantification yet. We propose a deep learning-based framework for learning an OGM algorithm which is both capable of quantifying uncertainty and which does not rely on manually labeled data. Results on synthetic and on real-world data show superiority over other approaches.

中文翻译：

---

基于模拟的证据占用网格映射的端到端学习框架

证据占用栅格地图（OGM）是自动驾驶汽车环境的一种流行表示形式。逆传感器模型（ISM）用于根据传感器数据（如激光雷达点云）计算OGM。几何ISM估计未观察到但可推断的区域中的状态时性能有限，并且难以处理模棱两可的输入。基于深度学习的ISM面临培训数据有限的挑战，并且它们通常还无法处理不确定性量化。我们提出了一种基于深度学习的框架，用于学习OGM算法，该算法既可以量化不确定性，又不依赖于手动标记的数据。综合数据和真实数据的结果显示出优于其他方法的优势。