Fang Zhao, Liu Youyu, Xu Lijin, Shahed Mahamudul Hasan, Shi Liping
School of Mechanical and Automotive Engineering, Anhui Polytechnic University, Wuhu 241000, China.
Anhui Gongchuang Industrial Robot Innovation Center Co., Ltd., Wuhu 241100, China.
Sensors (Basel). 2024 Sep 24;24(19):6185. doi: 10.3390/s24196185.
Laser point clouds are commonly affected by Gaussian and Laplace noise, resulting in decreased accuracy in subsequent surface reconstruction and visualization processes. However, existing point cloud denoising algorithms often overlook the local consistency and density of the point cloud normal vector. A feature map learning algorithm which integrates point normal constraints, Dirichlet energy, and coupled orthogonality bias terms is proposed. Specifically, the Dirichlet energy is employed to penalize the difference between neighboring normal vectors and combined with a coupled orthogonality bias term to enhance the orthogonality between the normal vectors and the subsurface, thereby enhancing the accuracy and robustness of the learned denoising of the feature maps. Additionally, to mitigate the effect of mixing noise, a point cloud density function is introduced to rapidly capture local feature correlations. In experimental findings on the anchor public dataset, the proposed method reduces the average mean square error (MSE) by 0.005 and 0.054 compared to the MRPCA and NLD algorithms, respectively. Moreover, it improves the average signal-to-noise ratio (SNR) by 0.13 DB and 2.14 DB compared to MRPCA and AWLOP, respectively. The proposed algorithm enhances computational efficiency by 27% compared to the RSLDM method. It not only removes mixed noise but also preserves the local geometric features of the point cloud, further improving computational efficiency.
激光点云通常会受到高斯噪声和拉普拉斯噪声的影响，导致后续曲面重建和可视化过程中的精度下降。然而，现有的点云去噪算法往往忽略了点云法向量的局部一致性和密度。提出了一种集成点法线约束、狄利克雷能量和耦合正交偏差项的特征图学习算法。具体来说，利用狄利克雷能量来惩罚相邻法向量之间的差异，并与耦合正交偏差项相结合，以增强法向量与子表面之间的正交性，从而提高特征图去噪学习的准确性和鲁棒性。此外，为了减轻混合噪声的影响，引入了点云密度函数以快速捕捉局部特征相关性。在锚点公共数据集上的实验结果表明，与MRPCA和NLD算法相比，该方法分别将平均均方误差（MSE）降低了0.005和0.054。此外，与MRPCA和AWLOP相比，它分别将平均信噪比（SNR）提高了0.13 DB和2.14 DB。与RSLDM方法相比，该算法的计算效率提高了27%。它不仅去除了混合噪声，还保留了点云的局部几何特征，进一步提高了计算效率。
