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

Tengfei Zhou,Xiaojun Cheng,Peng Lin,Ensheng Liu,Zhenlun Wu

doi:10.3390/rs12182923

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.

Highlights

In contrast to the traditional single-point acquisition method, terrestrial laser scanning (TLS) technology greatly improves work efficiency with a variety of applications [1,2,3,4]
In the absence of weights, the objective function to be minimized is obtained in the form: VTV = min Starting from this state of discussion, and taking random errors into consideration, the present study aims to achieve the following objectives: 1. Weight observations according to its corresponding prior information to solve the unknown parameters, for the sake of attenuating the effect of random errors on the coordinates of TLS; 2
We found that 55% of them decreased and 45% increased, and their magnitudes were in the order of 10−4 to 10−3, indicating that the general algorithm does improve the correlation of the parameters, but not very significantly, mainly due to the fact that the correlation of the parameters was ignored over the course of the data simulation

Summary

Introduction

In contrast to the traditional single-point acquisition method, terrestrial laser scanning (TLS) technology greatly improves work efficiency with a variety of applications [1,2,3,4]. Systematic errors, in the course of scanning, caused by ranging the angle of incident, target reflectivity, and temperature are undoubtedly not negligible [5]. All these above factors directly affect the accuracy of point cloud data [6], and to some extent, weaken the accuracy of subsequent point cloud processing. In the event that the nominal accuracy of the instrument loses consistency with the actual ones, the above operations may yield incorrect results, which is, extremely usual. Crucial to make a reasonable determination of the actual accuracy (or additional parameters), the so-called calibration (or self-calibration) of the instruments correctly and rationally

Methods

Results

Conclusion