LiDAR is a key enabling technology in growing autonomous markets, such as robotics, industrial, infrastructure, and automotive. LiDAR delivers precise 3D data about its environment in real time to provide “vision” for autonomous solutions. For autonomous vehicles (AVs), nearly every carmaker uses LiDAR to augment camera and radar systems for a comprehensive perception stack capable of safely navigating complex roadway environments. Computer vision systems can use the 3D maps generated by LiDAR sensors for object detection, object classification, and scene segmentation. Like any other supervised machine learning (ML) system, the point cloud data generated by LiDAR sensors should be labeled correctly in order for the ML model to make correct inferences. This allows AVs to operate smoothly and efficiently, avoiding incidents and collisions with objects, pedestrians, vehicles, and other road users.
In this post, we demonstrate how to label 3D point cloud data generated by Velodyne LiDAR sensors using Amazon

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