A General Point-Based Method for Self-Calibration of Terrestrial Laser Scanners Considering Stochastic Information

Abstract

Due to the existence of environmental or human factors, and because of the instrument itself, there are many uncertainties in point clouds, which directly affect the data quality and the accuracy of subsequent processing, such as point cloud segmentation, 3D modeling, etc. In this paper, to address this problem, stochastic information of point cloud coordinates is taken into account, and on the basis of the scanner observation principle within the Gauss–Helmert model, a novel general point-based self-calibration method is developed for terrestrial laser scanners, incorporating both five additional parameters and six exterior orientation parameters. For cases where the instrument accuracy is different from the nominal ones, the variance component estimation algorithm is implemented for reweighting the outliers after the residual errors of observations obtained. Considering that the proposed method essentially is a nonlinear model, the Gauss–Newton iteration method is applied to derive the solutions of additional parameters and exterior orientation parameters. We conducted experiments using simulated and real data and compared them with those two existing methods. The experimental results showed that the proposed method could improve the point accuracy from 10−4 to 10−8 (a priori known) and 10−7 (a priori unknown), and reduced the correlation among the parameters (approximately 60% of volume). However, it is undeniable that some correlations increased instead, which is the limitation of the general method.

Wand-Based Calibration of Unsynchronized Multiple Cameras for 3D Localization

“…So, we need to look for valid frames before t 1 or t 2 . The formula in the case of invalid frames can be obtained as shown in Equation (10), where Cam2[N t 1 ] represents the image frame of Cam2 corresponding to the valid timestamp t 1 and N t 1 represents the sequence number of the original image sequence of Cam2 at t 1 . Cam2[N t 2 ] means the same.…”

“…The localization accuracy of a UMCLS will be determined by the calibration accuracy of multiple cameras directly, so the process of multi-camera calibration is very important. According to the dimension of the calibration object, existing multi-camera calibration methods can be roughly divided into five kinds: methods based on 3D calibration objects [ 5 , 6 ], methods based on 2D calibration objects [ 7 , 8 ], methods based on 1D calibration objects [ 9 ], methods based on point objects [ 10 ], and self-calibration methods [ 11 , 12 , 13 , 14 , 15 , 16 ]. Note that methods based on 1D calibration objects can quickly and easily complete the calibration of multiple cameras without being affected by occlusion [ 17 ], so this paper chooses to use the 1D calibration method.…”

User Oriented Calibration Method for Stonex X300 Terrestrial Laser Scanner

“…At the same time, they found that the errors from standing tilt and laser beam tilt are considered to have a minor effect when adjusting model parameters. Later, a pointbased self-calibration method of TLS measurements was proposed by [14]. The method used a posterior estimation of the unknown calibration parameters to deliver more realistic modeling parameters relative to true accuracy than to nominal accuracy.…”

“…However, measurement uncertainty mainly depends on the algorithm applied and the investigated device type [5] [12]. The strategies can be classified into two groups: prior data acquisition (target-based) approaches and post-data acquisition and measurements (in-situ) approaches [13] [14]. The first group is called self-calibration, a target-based approach delivered from too many target measurements to guarantee the quality of the pre-defined targets' centers and thus increase the precision reliability of the calibration process [1].…”

Investigating Surface Fractures and Materials Behavior of Cultural Heritage Buildings Based on the Attribute Information of Point Clouds Stored in the TLS Dataset

“…In fact, it is extremely difficult to compensate for all these variables due to some local constraints, such as climatic condition (moisture and temperature pressures) and the tools' default settings from the manufacture. For example, the equipment calibration plays an important part in determining the performance evaluation and reducing the uncertainty in point clouds [52]. However, factors such as the end users (audience) of 3D scanning technology targeted in this study, most of whom are architects and heritage conservators, are less applicable (less priority) to equipment calibration due to the prerequisite knowledge of 3D processing datasets.…”