Semantic grids are a useful representation of the environment around a robot. They can be used in autonomous vehicles to concisely represent the scene around the car, capturing vital information for downstream tasks like navigation or collision assessment. Information from different sensors can be used to generate these grids. Some methods rely only on RGB images, whereas others choose to incorporate information from other sensors, such as radar or LiDAR. In this paper, we present an architecture that fuses LiDAR and camera information to generate semantic grids. By using the 3D information from a LiDAR point cloud, the LiDAR-Aided Perspective Transform Network (LAPTNet) is able to associate features in the camera plane to the bird's eye view without having to predict any depth information about the scene. Compared to state-of-theart camera-only methods, LAPTNet achieves an improvement of up to 8.8 points (or 38.13%) over state-of-art competing approaches for the classes proposed in the NuScenes dataset validation split.

本文提出了一种融合了激光雷达和相机信息的语义栅格生成架构，即LiDAR-Aided Perspective Transform Network（LAPTNet），通过使用LiDAR点云的3D信息，LAPTNet能够将相机平面中的特征与鸟瞰图中的特征相匹配，而无需预测场景深度信息，相较于最先进的仅使用相机的方法，在NuScenes数据集验证集中，LAPTNet对于所提出的类别比竞争方法的改进高达8.8个点（或38.13％）。

LAPTNet：激光雷达辅助视角变换网络