基于置信度的飞行时间点云去噪方法

Time-of-Flight Point Cloud Denoising Method Based on Confidence Level

飞行时间(Time-of-Flight,ToF)三维成像方法由于多路径干扰和混合像素等问题降低了目标物体深度测量的精度。传统的方法通过优化重构异常点云数据或滤除噪声点云数据来提高目标的准确性,但是这些方法复杂度高且容易导致过度平滑。三维点云图像中的有效点云与噪声点云之间的关系很难用数学模型来表示。针对上述问题,本文提出了一种基于置信度的飞行时间点云去噪方法。首先,分析多帧点云数据的概率相关性,以点云数据的置信度作为判别有效点云与噪声点云的依据;其次,利用多帧点云之间的矢量对偶性,提出了一种快速提取不同置信度点云的算法,其时间复杂度为O(L);最后使用该算法提取多帧三维图像中置信度高的点云数据获得目标物体的真实测量数据,并重点对4组不同场景的点云数据进行对比实验。实验结果表明,该算法能够在有效滤除噪声的同时,显著提高目标物体的距离测量精度,增强目标物体的特征,因此具有广泛的应用价值。

The time-of-flight(ToF) 3D imaging method suffers from reduced precision in the depth measurement of target objects because of multipath interference and mixed pixels. Traditional methods improve the accuracy of the measurement by optimizing and reconstructing abnormal point cloud data or filtering noisy point cloud data. However, these methods are complex and can easily lead to excessive smoothing. The relationship between a valid point cloud and noisy point cloud in a 3D point cloud image is difficult to express using a mathematical model. To address these problems, a point cloud denoising method based on the confidence level is proposed in this paper. First, the probability correlation of multi-frame point cloud data is analyzed, and the confidence level of the point cloud data is used as the basis to distinguish valid point clouds from noisy point clouds. Second, by utilizing the vector duality between multi-frame point clouds,a fast algorithm for extracting point clouds with different confidence levels is presented, and its time complexity is O(L). Finally, the algorithm is used to extract the point cloud data with a high confidence level in multi-frame 3D images to obtain the real measurement data of the target object. We focus on the comparative experiments of four groups of point cloud data in different scenes. The experimental results show that the algorithm can not only effectively filter the noise but also significantly improve the distance measurement accuracy of the target object and enhance the characteristics of the target object;therefore, it has extensive application value.