Explanation for the seam line discontinuity in terrestrial laser scanner point clouds

The Photogrammetric Record

2021

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).

Section: Experiments Designsupporting

confidence: 89%

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

Maalek

The Photogrammetric Record

2021

“…A circular patch around each target was manually extracted from the point cloud. The centre coordinates were estimated using the method described by Lichti et al (2019b). Past experience with this instrument has demonstrated that millimetre-level 3D coordinate accuracy can be achieved in a similar environment with comparable dimensions (Lichti et al, 2019a).…”

Section: Accuracy Assessmentmentioning

confidence: 99%

Geometric modelling and calibration of a spherical camera imaging system

The Photogrammetric Record

Jarron

Tredoux

et al. 2020

The Ladybug5 is an integrated, multi-camera system that features a near spherical field of view. It is commonly deployed on mobile mapping systems to collect imagery for 3D reality capture. This paper describes an approach for the geometric modelling and self-calibration of this system. The collinearity equations of the pinhole camera model are augmented with five radial lens distortion terms to correct the severe barrel distortion. Weighted relative orientation stability constraints are added to the self-calibrating bundle adjustment solution to enforce the angular and positional stability between the Ladybug5's six cameras. Results are presented from two calibration datasets and an independent dataset for accuracy assessment. It is demonstrated that centimetre-level 3D reconstruction accuracy can be achieved with the proposed approach. Moreover, the effectiveness of the lens distortion modelling is demonstrated. Image-space precision and object-space accuracy are improved by 92% and 93%, respectively, relative to a two-term model. The high correlations between lens distortion coefficients were not found to be detrimental to the solution. The mechanical stability of the system was assessed by comparing calibrations taken before and after ten months of routine camera system use. The results suggest sub-pixel interior orientation stability and millimetre-level relative orientation stability. Analyses of accuracy and parameter correlation demonstrate that a slightly relaxed weighting strategy is preferred to tightly-enforced relative orientation stability constraints.

“…From the figures, it is known that the increments of random noises from 0 to ±8 mm do not lead to large increases in the RMSEs. Results of scanner calibrations published recently [27][28][29][30][31] indicated that the point cloud errors are normally less than 8 mm within a scan range of 10 m. As a result, it is deduced that the proposed model can work well with half of the joint scanned from a single scan. The RMSE of the translational, rotational, and dimensional parameters for Joint (a) from 10° to 360° coverage and from noise level 0 mm to ± 8 mm is shown in Figures 12, 13, and 14, respectively.…”

Section: Accuracy Analysismentioning

confidence: 94%

Geometric Modelling for 3D Point Clouds of Elbow Joints in Piping Systems

Chan

Xia

et al. 2020

Sensors

Pipe elbow joints exist in almost every piping system supporting many important applications such as clean water supply. However, spatial information of the elbow joints is rarely extracted and analyzed from observations such as point cloud data obtained from laser scanning due to lack of a complete geometric model that can be applied to different types of joints. In this paper, we proposed a novel geometric model and several model adaptions for typical elbow joints including the 90° and 45° types, which facilitates the use of 3D point clouds of the elbow joints collected from laser scanning. The model comprises translational, rotational, and dimensional parameters, which can be used not only for monitoring the joints’ geometry but also other applications such as point cloud registrations. Both simulated and real datasets were used to verify the model, and two applications derived from the proposed model (point cloud registration and mounting bracket detection) were shown. The results of the geometric fitting of the simulated datasets suggest that the model can accurately recover the geometry of the joint with very low translational (0.3 mm) and rotational (0.064°) errors when ±0.02 m random errors were introduced to coordinates of a simulated 90° joint (with diameter equal to 0.2 m). The fitting of the real datasets suggests that the accuracy of the diameter estimate reaches 97.2%. The joint-based registration accuracy reaches sub-decimeter and sub-degree levels for the translational and rotational parameters, respectively.