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Refinement of Edge Extraction Based on Lidar Point Cloud

机译:基于LIDAR点云的边缘提取改进

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Lidar has been used in many areas of computer vision and robotics, like 3D reconstruction, autonomous navigation and positioning. Extracting accurate edges for 3d point clouds plays a key role in some applications. However, few people are concerned about this issue. In this paper, we present a novel pipeline for refining the position of the edge using the rotation of the lidar itself and its depth discontinuity at the edges. Our method consists of two parts. First, edge points are extracted according to the depth difference in the reference frame of point cloud. Then, we apply our refining strategy to obtain the more accurate edge points. The novelty is that our approach compensates for hardware deficiencies through algorithms, improving the accuracy of lidar edge points. The experiment provides comparisons of the edge points extracted by the direct method and our method.
机译:LIDAR已被用于计算机视觉和机器人的许多领域,如3D重建,自主导航和定位。提取3D点云的准确边缘在某些应用中扮演关键作用。但是,很少有人担心这个问题。在本文中,我们介绍了一种新型管道,用于使用激光雷达本身的旋转和边缘处的深度不连续性来改进边缘的位置。我们的方法由两部分组成。首先,根据点云的参考帧中的深度差来提取边缘点。然后,我们应用我们的炼油策略来获得更准确的边缘点。新颖性是,我们的方法通过算法来补偿硬件缺陷,提高了激光雷达边缘点的准确性。实验提供了通过直接方法和方法提取的边缘点的比较。

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