论文标题
3D对象检测方法基于YOLO和K均值的图像和点云
3D Object Detection Method Based on YOLO and K-Means for Image and Point Clouds
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点云数据重建。但是，3D点云中的实时检测仍然需要强大的算法。本文提出了一种基于点云和图像的3D对象检测方法，该方法由其中的部分组成。（1）LIDAR-CAMERA校准和未发生的图像转换。 （2）基于YOLO的检测和点云提取，（3）基于k均值的点云分割和检测实验测试和深度图像评估。在我们的研究中，相机可以捕获图像以通过使用Yolo来实时2D对象检测，我们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可以实现GPU中的高速3D对象识别函数。 K-均值聚类在点云中之后，准确性和精度会引起。我们检测方法的速度比PointNet更快。
Lidar based 3D object detection and classification tasks are essential for autonomous driving(AD). A lidar sensor can provide the 3D point cloud data reconstruction of the surrounding environment. However, real time detection in 3D point clouds still needs a strong algorithmic. This paper proposes a 3D object detection method based on point cloud and image which consists of there parts.(1)Lidar-camera calibration and undistorted image transformation. (2)YOLO-based detection and PointCloud extraction, (3)K-means based point cloud segmentation and detection experiment test and evaluation in depth image. In our research, camera can capture the image to make the Real-time 2D object detection by using YOLO, we 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 can achieve High-speed 3D object recognition function in GPU. The accuracy and precision get imporved after k-means clustering in point cloud. The speed of our detection method is a advantage faster than PointNet.
