Fca US LLC

Alex M Smith

Dalong Li

Stephen W Horton

A semi-automatic three-dimensional light detection and ranging (LIDAR) point cloud data annotation system and method for autonomous driving of a vehicle involve filtering 3D LIDAR point cloud and normalizing the filtered 3D LIDAR point cloud data relative to the vehicle to obtain normalized 3D LIDAR point cloud data, quantizing the normalized 3D LIDAR point cloud data by dividing it into a set of 3D voxels, projecting the set of 3D voxels to a 2D birdview, identifying a possible object by applying clustering to the 2D birdview projection, obtaining an annotated 2D birdview projection including annotations by a human annotator via the annotation system regarding whether the bounding box corresponds to a confirmed object and a type of the confirmed object, and converting the annotated 2D birdview projection to back into annotated 3D LIDAR point cloud data.

Semi-automatic LIDAR annotation system for autonomous driving

An advanced driver assistance system (ADAS) and method for a vehicle utilize a light detection and ranging (LIDAR) system configured to emit laser light pulses and capture reflected laser light pulses collectively forming three-dimensional (3D) LIDAR point cloud data and a controller configured to receive the 3D LIDAR point cloud data, convert the 3D LIDAR point cloud data to a two-dimensional (2D) birdview projection, obtain a template image for object detection, the template image being representative of a specific object, blur the 2D birdview projection and the template image to obtain a blurred 2D birdview projection and a blurred template image, and detect the specific object by matching a portion of the blurred 2D birdview projection to the blurred template image.

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LaSalle, Canada

Alex M Smith