论文标题
基于图像和点云的基于YOLO和K-MEANS的3D对象检测方法
YOLO and K-Means Based 3D Object Detection Method on Image and Point Cloud
论文作者
论文摘要
基于激光雷达的3D对象检测和分类任务对于自动驾驶（AD）至关重要。激光雷达传感器可以提供周围环境的3D点数据重建。但是3D点云中的检测仍然需要强烈的算法挑战。本文由三个部分组成。（1）LIDAR-CAMERA CALIB。 （2）基于YOLO的，基于检测和点云提取，（3）基于k均值的点云分割。在我们的研究中，相机可以捕获图像以通过使用Yolo进行实时2D对象检测，我将边界框传输到函数正在从LIDAR上进行3D对象检测的节点。通过比较从3D点传输的2D坐标是否在对象边界框中，并且进行K-Means聚类可以在GPU中获得高速3D对象识别函数。
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.
