In view of the defects of total station and laser tracker in the construction of three-dimensional control network, a construction method of three-dimensional control network is proposed by combining total station and laser tracker. Total stations and laser trackers are utilized separately to observe the control points according to the free-stationing method. In data processing, the weights of different types of observations of total station and laser tracker are reasonably determined based on the Helmert variance component estimation. The high-precision horizontal direction observations of total station, which are better than 0 . 5 ″ , and the ultra-high-precision ranging observations of laser tracker, which are better than 10 μm, are effectively combined in order to obtain more precise adjustment results. The experimental results show that the positional precision of the combined adjustment is better than that of the adjustment results of a single measurement system, and the precision of three-dimensional control network can be further improved by employing Helmert variance component estimation to determine the weight reasonably.

Automatic pairwise registration for unordered terrestrial laser scanner (TLS) point clouds is the pre-requisite for many applications, including 3D model reconstruction, cultural heritage management and landslide monitoring. However, most of the existing registration methods are still suffering from several limitations: (1) difficult to find the correct corresponding point pairs from the source and target point cloud, (2) low accuracy of pairwise registration and (3) the iterative process of registration is time-consuming. To overcome these challenges, based on the covariance matrix, this paper presents a robust and descriptive feature descriptor vector (FDV) to locally describe a point, by which the corresponding point pairs are obtained and the registration matrix is calculated. First, the FDVs of the original point cloud’s keypoints are calculated. Second, the corresponding point pairs are found via their FDVs. Third, the coarse transformation matrix is calculated by the corresponding point pairs using the singular value decomposition (SVD) algorithm, and which is further refined by the iterative closest point (ICP) algorithm to get a better result. Finally, the experiments are conducted on the Autonomous Systems Lab (ASL) and ISPRS datasets; our results show that the proposed algorithm can obtain good registration performance, and outperforms the compared methods.

A new type of dot-distribution coded targets for digital industrial photogrammetry was presented in the paper. The coded target was composed of 5 template points and 3 code points. Different character strings were used as coder for each coded target, and the max capacity was 496. Affine transformation and cross ratio were adopted to decode the targets automatically and rapidly. Experiments proved that this type of dot-distribution coded targets fulfill the requirements of automatic digital industrial photogrammetry.

Because the M estimation method may lead to poor robustness or even failure owing to excessive outliers, a robust algorithm with a high breakdown point was proposed and applied to the estimation of coordinate transformation parameters. Firstly, the sampling method was used to calculate multiple sets of model parameters, and some sampling results were sifted according to posterior information. Then, the samples were sorted according to their number in the sampling results, and the F-test was adopted to screen and reserve valid information. Finally, the initial values of the reliable parameters were computed using the valid information, and the final parameters were obtained by the Institute of Geodesy and Geophysics III scheme. Monte Carlo method was adopted for the simulation test, and a case analysis was chosen for verification. The results show that the proposed method can identify and process outliers more accurately than those of Rousseeuw and Hubert (2011 Wires Data Min. Knowl. 1 73–79) and Tao et al (2016 Acta Geod. Cartogr. Sin. 45 297–301). When the proportion of outliers exceeded 50%, the proposed algorithm maintained a strong robustness and had a high breakdown point.

To obtain the complete coverage when scanning a large-scale scene using a laser scanner, registration of pairwise point clouds by the corresponding geometric features is usually necessary under the condition of insufficient direct geographic reference information of GNSS sensors. In this paper, we propose a target-less coarse registration method for pairwise point clouds using the corresponding voxel-based planes, which are identified by a pair of conjugate 2-plane bases. The voxel-based planes are firstly extracted from the entire point cloud with 3D cubic grids decomposed by the octree-based voxelization; Then, the 2-plane bases in each point cloud are constructed, and by comparing the dihedral angles of two 2-plane bases that come from the source and target point clouds, respectively, the conjugate 2-plane base pairs are generated one by one; Next, a set of plane correspondences is identified by a pair of conjugate 2-plane bases, and its corresponding largest consistency planes (LCP) set is calculated; Finally, a series of plane correspondence sets are obtained using the generated pairs of conjugate 2-plane bases, and the one with the highest LCP is used to compute the transformation matrix between the pairwise point clouds. Experimental results revealed that our proposed pairwise coarse registration method can be effective for aligning point clouds acquired from indoor and outdoor scenes, with rotation errors less than 0.4 degrees, translation errors less than 0.4 m, root mean square distance (RMSD) less than 0.42 m, and successful registration rate (SRR) about 98%. Furthermore, our proposed method was more efficient than the point- and line-based methods under the same hardware and software configuration conditions.

After using a terrestrial laser scanner to acquire building facade point clouds, the extraction of feature lines can simplify the expression of building objects, thereby contributing to the accurate construction of building facade geometric models. To address the problems of missing extraction and low accuracy in existing methods, this study proposes a feature line extraction method for building facade point clouds by exploring the different spatial topological relationship between feature and non‐feature points. The method comprises three steps: feature point extraction, feature line generation, and feature line merging and optimization. First, feature points are extracted using the convex hull distance value and relative angle, as defined in this study. Thereafter, feature points are clustered by combining the random sample consensus and region growing algorithms, and the feature lines are obtained by utilizing the iterative weighted least squares method based on the IGGIII weight function. Finally, the feature lines are merged and optimized using an endpoint search method to improve the discontinuity and missing common endpoints of the original feature lines. The experimental results obtained using simulated and measured point cloud data show that this method can accurately extract feature points, and the extracted feature lines obtained from building facade point clouds have better accuracy and completeness, which is more practical than the existing methods and can be used in many applications such as building facade measurement and urban three‐dimensional modeling.