Lidar produces three-dimensional point clouds. From the point clouds, objects need to be detected and tracked. For example, from the point clouds, embodiments detect what kind of objects is detected, what shape the object is, and the objects current location and trajectory. Each lidar sensor on a vehicle produces a point cloud periodically. The point cloud is input into a deep learning neural network that outputs road geometry, such as road edges and lane dividers. In some embodiments, the other network can also output information about other objects in the environment, such as other vehicles and pedestrians.
USING DEEP LEARNING TO IDENTIFY ROAD GEOMETRY FROM POINT CLOUDS
10.04.2025
Patent
Elektronische Ressource
Englisch
Road extraction from mobile laser scanning point clouds
British Library Conference Proceedings | 2022
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