Terrestrial laser scanning is used in many disciplines of engineering. Examples include mobile mapping, architecture surveying, archaeology, as well as monitoring and surveillance measurements. For most of the mentioned applications, 3D object capturing in an accuracy range of several millimeters up to a few centimeters is sufficient. However, in engineering geodesy, particularly in industrial surveying or monitoring measurements, accuracies in a range of a few millimeters are required. Additional increased quality requirements apply to these applications. This paper focuses on the quality investigation of data captured with static and kinematic terrestrial laser scanning. For this purpose, suitable sensors, which are typically used in the approach of a multi-sensor-system, as well as the corresponding data capturing/acquisition strategies, are presented. The aim of such systems is a geometry- and surface-based analysis in an industrial environment with an accuracy of +/− 1–2 mm or better.

Terrestrial laser scanning (TLS) is an efficient solution to collect large-scale data. The efficiency can be increased by combining TLS with additional sensors in a TLS-based multi-sensor-system (MSS). The uncertainty of scanned points is not homogenous and depends on many different influencing factors. These include the sensor properties, referencing, scan geometry (e.g., distance and angle of incidence), environmental conditions (e.g., atmospheric conditions) and the scanned object (e.g., material, color and reflectance, etc.). The paper presents methods, infrastructure and results for the validation of the suitability of TLS and TLS-based MSS. Main aspects are the backward modelling of the uncertainty on the basis of reference data (e.g., point clouds) with superordinate accuracy and the appropriation of a suitable environment/infrastructure (e.g., the calibration process of the targets for the registration of laser scanner and laser tracker data in a common coordinate system with high accuracy) In this context superordinate accuracy means that the accuracy of the acquired reference data is better by a factor of 10 than the data of the validated TLS and TLS-based MSS. These aspects play an important role in engineering geodesy, where the aimed accuracy lies in a range of a few mm or less.

The use of multisensor system (MSS) plays a key role in engineering geodesy. Because of the complexity of tasks such as industrial applications with high accuracy requirements for heterogeneous and efficient three-dimensional (3D) data acquisition, kinematic MSS are often used. Traditionally, these MSS are composed of referencing sensors and object-capturing sensors. The crucial point in a MSS setup is the determination of the mutual position and orientation [six degrees of freedom (6 DOF)] of each sensor. Within this contribution, a possibility for the determination of the 6 DOF of light detection and ranging (LiDAR) sensors in MSS is introduced. The presented approach is generally applicable and allows the 6 DOF determination of profile laser scanners. The 6 DOF and their uncertainty measures are estimated within an adjustment model by utilizing known reference geometries (RFGs). The approach is especially of interest when sensor origins are not physically available and measurable. It is generally applicable in a static or kinematic measurement environment. As an example, the approach is applied in an industrial environment with accuracy requirements of a few millimeters. The used MSS consists of a terrestrial laser scanner (object capturing) and a laser tracker (referencing). The linking component is a tracker-machine control sensor, which is attached to the laser scanner. More precisely, 6 DOF between the tracker-machine control sensor and the origin of the laser scanner have to be determined. Depending on the required accuracy of the 3D object acquisition, the determination of the 6 DOF must fulfill high accuracy requirements. Finally, the results and the accuracy of the 6 DOF determination are shown and validated with a static calibration procedure.

In recent years, the requirements in the industrial production, e.g., ships or planes, have been increased. In addition to high accuracy requirements with a standard deviation of 1 mm, an efficient 3D object capturing is required. In terms of efficiency, kinematic laser scanning (k-TLS) has been proven its worth in recent years. It can be seen as an alternative to the well established static terrestrial laser scanning (s-TLS). However, current k-TLS based multi-sensor-systems (MSS) are not able to fulfil the high accuracy requirements. Thus, a new k-TLS based MSS and suitable processing algorithms have to be developed. In this contribution a new k-TLS based MSS will be presented. The main focus will lie on the (geo-)referencing process. Due to the high accuracy requirements, a novel procedure of external (geo-)referencing is used here. Hereby, a mobile platform, which is equipped with a profile laser scanner, will be tracked by a laser tracker. Due to the fact that the measurement frequency of the laser scanner is significantly higher than the measurement frequency of the laser tracker a direct point wise (geo-)referencing is not possible. To enable this a Kalman filter model is set up and implemented. In the prediction step each point is shifted according to the determined velocity of the platform. Because of the nonlinear motion of the platform an iterative extended Kalman filter (iEKF) is used here. Furthermore, test measurements of a panel with the k-TLS based MSS and with s-TLS were carried out. To compare the results, the 3D distances with the M3C2-algorithm between the s-TLS 3D point cloud and the k-TLS 3D point cloud are estimated. It can be noted, that the usage of a system model for the (geo-)referencing is essential. The results show that the mentioned high accuracy requirements have been achieved.

Rigorous statistical testing of deformation using a terrestrial laser scanner (TLS) can avoid events such as structure collapses. Such a procedure necessitates an accurate description of the TLS measurements’ noise, which should include the correlations between angles. Unfortunately, these correlations are often unaccounted for due to a lack of knowledge. This contribution addresses this challenge. We combine (i) a least-square approximation to extract the geometry of the TLS point cloud with the aim to analyze the residuals of the fitting and (ii) a specific filtering coupled with a maximum likelihood estimation to quantify the amount of flicker noise versus white noise. This allows us to set up fully populated variance covariance matrices of the TLS noise as a result.