  Semantic grids can be useful representations of the scene around an
autonomous system. By having information about the layout of the space around
itself, a robot can leverage this type of representation for crucial tasks such
as navigation or tracking. By fusing information from multiple sensors,
robustness can be increased and the computational load for the task can be
lowered, achieving real time performance. Our multi-scale LiDAR-Aided
Perspective Transform network uses information available in point clouds to
guide the projection of image features to a top-view representation, resulting
in a relative improvement in the state of the art for semantic grid generation
for human (+8.67%) and movable object (+49.07%) classes in the nuScenes
dataset, as well as achieving results close to the state of the art for the
vehicle, drivable area and walkway classes, while performing inference at 25
FPS.
