  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.
