  Lidar based 3D object detection and classification tasks are essential for
automated driving(AD). A Lidar sensor can provide the 3D point coud data
reconstruction of the surrounding environment. But the detection in 3D point
cloud still needs a strong algorithmic challenge. This paper consists of three
parts.(1)Lidar-camera calib. (2)YOLO, based detection and PointCloud
extraction, (3) k-means based point cloud segmentation. In our research, Camera
can capture the image to make the Real-time 2D Object Detection by using YOLO,
I transfer the bounding box to node whose function is making 3d object
detection on point cloud data from Lidar. By comparing whether 2D coordinate
transferred from the 3D point is in the object bounding box or not, and doing a
k-means clustering can achieve High-speed 3D object recognition function in
GPU.
