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.

The strong and complex vibration from a manipulator for anchor drilling will damage the key components of the manipulator and produce noise at the same time. According to its vibration characteristics, a fluid–spring vibration absorption approach with five degrees of freedom (DOFs) is proposed, which has perfect symmetry, and a vibration absorber was designed with a symmetrical structure. Employing the generalization formula of the Bernoulli equation and dynamic equation, a fluid–spring coupling damping coefficient equation was constructed. Vibration transmissibility was used as the evaluation index of vibration absorption performance. The elastic coefficients of the tension spring and torsion spring, the area ratio of circular through-holes to the vibration-absorbing plate, and the radius of circular through-holes were the main independent factors influencing the damping coefficients. An orthogonal experiment with four factors and four levels was designed. Using FLUENT and SIMULATION to implement joint simulations, the distribution law of the flow fields and the damping coefficients of each approach were obtained, and then the best combination of factors was selected. Taking a manipulator used for anchor drilling in Huainan of China as a case study, using the designed fluid–spring vibration absorber, the vibration displacements in the five DOFs were reduced by 68.32%, 49.82%, 52.17%, 49.01%, and 57.09% respectively, indicating a good vibration absorption performance with symmetry about the $\overrightarrow{z}$-axis.