Automated calibration of smartphone cameras for 3D reconstruction of mechanical pipes

This paper outlines a new framework for the calibration of optical instruments, in particular smartphone cameras, using highly redundant circular black-and-white target fields. New methods were introduced for (i) matching targets between images; (ii) adjusting the systematic eccentricity error of target centres; and (iii) iteratively improving the calibration solution through a free-network self-calibrating bundle adjustment. The proposed method effectively matched circular targets in 270 smartphone images, taken within a calibration laboratory, with robustness to type II errors (false negatives). The proposed eccentricity adjustment, which requires only camera projective matrices from two views, behaved comparably to available closed-form solutions, which require additional a priori object-space target information. Finally, specifically for the case of mobile devices, the calibration parameters obtained using the framework were found to be superior compared to in situ calibration for estimating the 3D reconstructed radius of a mechanical pipe (approximately 45% improvement on average)

A Comparison of Three Geometric Self-Calibration Methods for Range Cameras

Significant instrumental systematic errors are known to exist in data captured with range cameras using lock-in pixel technology. Because they are independent of the imaged object scene structure, these errors can be rigorously estimated in a self-calibrating bundle adjustment procedure. This paper presents a review and a quantitative comparison of three methods for range camera self-calibration in order to determine which, if any, is superior. Two different SwissRanger range cameras have been calibrated using each method. Though differences of up to 2 mm (in object space) in both the observation precision and accuracy measures exist between the methods, they are of little practical consequence when compared to the magnitude of these measures (12 mm to 18 mm). One of the methods was found to underestimate the principal distance but overestimate the rangefinder offset in comparison to the other two methods whose estimates agreed more closely. Strong correlations among the rangefinder offset, periodic error terms and the camera position co-ordinates are indentified and their cause explained in terms of network geometry and observation range

A self-calibration of the Leica Scan Station C10 scanner

Similar to other surveying instruments, the observed data from terrestrial laser scanner (TLS) can be impaired with errors. Then, calibration routine is necessary for the TLS to ensure the quality of the data and also to make it applicable for surveying applications. There are two calibration approaches available: 1) component, and 2) system calibration. Due to the requirement of special laboratories and tools to perform component calibration, then this approach cannot be implemented by most of the TLS users. In contrast, system calibration that can be performed through self-calibration is more convenient and the requirements (e.g. room with targets) are easier to be provided. Self-calibration bundle adjustment is carry out using measured spherical coordinates (e.g. distance, horizontal and vertical angles) as observations. In extension to the functional model of each observation, a set of calibration parameters was used, which were determined in a self-calibration procedure. These parameters are derived from well-known error sources of geodetic instruments as constant (a0), collimation axis (b0), trunnion axis (b1) and vertical circle index (c0) errors. Self-calibration was performed for Leica ScanStation C10 at laboratory with dimension 9m × 7m × 2.6m and 130 black and white targets were fairly distributed. Data obtained from seven scan station were processed and statistical analysis (e.g. t-test) has shown that only collimation axis (77.1 inch) and vertical circle index (-62.4 inch) errors are significant for the calibrated scanner

A Practical Algorithm for the Viewpoint Planning of Terrestrial Laser Scanners

Applications using terrestrial laser scanners (TLS) have been skyrocketing in the past two decades. In a scanning project, the configuration of scans is a critical issue as it has significant effects on the project cost and the quality of the product. In this paper, a practical strategy is proposed to resolve the problem of the optimal placement of the terrestrial laser scanner. The method attempts to reduce the number of viewpoints under the premise that the scenes are fully covered. In addition, the approach is designed in a way that the solutions can be efficiently explored. The method has been tested on 540 polygons simulated with different sizes and complexities. The results have also been compared with a benchmark strategy in terms of the optimality of the solutions and runtime. It is concluded that our proposed algorithm ties or reduces the number of viewpoints in the benchmark paper in 85.6% of the 540 tests. For complex environments, the method can potentially reduce the project cost by 10%. Although with relatively lower efficiency, our method can still reach the solution within a few minutes for a polygon with up to 500 vertices

Temporal Stability of the Velodyne HDL-64E S2 Scanner for High Accuracy Scanning Applications

The temporal stability and static calibration and analysis of the Velodyne HDL‑64E S2 scanning LiDAR system is discussed and analyzed. The mathematical model for measurements for the HDL-64E S2 scanner is updated to include misalignments between the angular encoder and scanner axis of rotation, which are found to be a marginally significant source of error. It is reported that the horizontal and vertical laser offsets cannot reliably be obtained with the current calibration model due to their high correlation with the horizontal and vertical offsets. By analyzing observations from two separate HDL-64E S2 scanners it was found that the temporal stability of the horizontal angle offset is near the quantization level of the encoder, but the vertical angular offset, distance offset and distance scale are slightly larger than expected. This is felt to be due to long term variations in the scanner range, whose root cause is as of yet unidentified. Nevertheless, a temporally averaged calibration dataset for each of the scanners resulted in a 25% improvement in the 3D planar misclosure residual RMSE over the standard factory calibration model

Automatic Object Extraction from Electrical Substation Point Clouds

The reliability of power delivery can be profoundly improved by preventing wildlife-related power outages. This can be achieved by insulating electrical substation components with non-conductive covers. The manufacture of custom-built covers requires as-built models of the salient components. This study presents new, automated methodology to recognize key components of electrical substations from 3D LiDAR data acquired using terrestrial laser scanning. The proposed methodology includes six novel algorithms to recognize key components (fence, cables, circuit breakers, bushings and bus pipes) of electrical substations. Three datasets with different resolutions and configurations are used in this study. A Leica HDS 6100 laser scanner was used to acquire the first dataset and a Faro Focus3D laser scanner was employed to collect the second and third datasets. The obtained results indicate that 178 and 171 out of 181 electrical substation elements were successfully recognized in the first and second dataset, respectively, and 183 out of 191 components were identified in the third dataset. The results also demonstrate that an average 97.8% accuracy and average 98.8% precision at the point cloud level can be achieved