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After more than twenty years of commercial use, laser scanners have reached technical maturity and consequently became a standard tool for 3D-data acquisition across various fields of application. Yet, meaningful stochastic information regarding the achieved metric quality of recorded points remains an open research question. Recent research demonstrated that raw intensity values can be deployed to derive stochastic models for reflectorless rangefinders. Yet, all existing studies focused on single instances of particular laser scanners while the derivation of the stochastic models required significant efforts.Motivated by the aforementioned shortcomings, the focus of this study is set on the comparison of stochastic models for a series of eight identical phase-based scanners that differ in age, working hours and date of last calibration. In order to achieve this, a standardised methodological workflow is suggested to derive the unknown parameters of the individual stochastic models. Based on the generated outcome, a comparison is conducted which clarifies if a universally applicable stochastic model (type calibration) can be used for a particular scanner model or if individual parameter sets are still required for every scanner (instance calibration) to validate the practical benefit and usability of those models. The generated results successfully demonstrate that the computed stochastic model is transferable to all individual scanners of the series.

Analyzing objects concerning their periodic behavior is mostly performed with inertial measurement units (IMUs) or global navigation satellite system (GNSS) sensors fixed to its surface. For connecting observations, sensors have to be assigned to the same reference frame in space and time as a prerequisite. Using light detection and ranging (LiDAR) observations enables contactless, time-synchronized, and spatially connected data points within a single sensor. Therefore, common signal properties are further analyzed in the spectrum to find connections and similarities between observations. Since observations are spatially continuous we can discretize them and traditionally process them. However, the time domain offers a diversity of ways to simultaneously estimate frequencies and continuously model properties at different spatial locations. Within this work, we exploit the potential of processing LiDAR data in the time domain to make use of the sensor’s contactless observations and its sampling rate in space and time. Consecutive points and their spatial neighborhoods are used to implement temporal as well as spatiotemporal connections to directly model oscillations in 2-D space. Moreover, we compute an uncertainty of estimated variables to qualify our solution. Consequently, our approach offers the opportunity to describe as well as evaluate movements and vibrations of spatially connected areas.

This paper introduces methods for monitoring rock slope movements in Alpine environments based on terrestrial images. The first method is a photogrammtric point cloud-based deformation analysis, relying on M3C2. Although effective in identifying large changes, the method has a tendency to underestimate smaller-scale movements. A feature-based method is presented to address this limitation, using SIFT features to track keypoints in images from different epochs. These automatically detected 3D vectors offer high spatial density and enable small-scale movement detection in the order of a few millimeters. The results are incorporated into a deformation analysis that allows statistically based conclusions about the ongoing movements. The workflow relies on georegistration using Ground Control Points. To investigate the possibility of avoiding these points, a registration method based on the ICP algorithm and M3C2 is tested. The study utilizes data from an active landslide site at Hochvogel Mountain in the Alps, analyzing changes and deformations from 2018 to 2021, revealing an average motion of 75 mm.

Landslides represent a significant natural hazard with wide-reaching impacts. Addressing the challenge of accurately detecting and monitoring landslides, this research introduces a novel approach that combines feature tracking with histogram analysis for efficient outlier removal. Distinct from existing methods, our approach leverages advanced histogram techniques to significantly enhance the accuracy of landslide detection, setting a new standard in the field. Furthermore, when tested on three different data sets, this method demonstrated a notable reduction in outliers by approximately 15 to 25 percent of all displacement vectors, exemplifying its effectiveness. Key to our methodology is a refined feature tracking process utilizing terrestrial laser scanners, renowned for their precision and detail in capturing surface information. This enhanced feature tracking method allows for more accurate and reliable landslide monitoring, representing a significant advancement in geospatial analysis techniques.

The rising global energy demand has made it essential to reduce energy consumption in the residential building stock. Adding a new insulating envelope with Renewable Energy Sources (RESs) onto the existing buildings’ facade is one way to achieve zero energy consumption in residential buildings. The ENSNARE project aims to semi-automate this process by using prefabricated facade modules and developing new building data acquisition techniques. Prior to this research project, an analysis was carried out and several research gaps were identified. One of the obstacles to using prefabricated modules with RES is that the layout needs to be drawn and adjusted during different phases of the project. That is time-consuming. For this reason, this article describes two new solutions: (1) automated drafting of the optimized layout of prefabricated modules of the facade and the number of solar panels based on the existing residential building model, and (2) automated adjustment of the layout depending on the phase of the renovation project and the accuracy of the measurements in each step. The proposed semi-automated approach has the potential to significantly reduce the time used in drafting the layout of the prefabricated modules, which benefits the whole renovation process, contributing to a more sustainable future for the residential building stock.

In this work, we construct a Kd–Octree hybrid index structure to organize the point cloud and generate patch-based feature descriptors at its leaf nodes. We propose a simple yet effective convolutional neural network, termed KdO-Net, with Kd–Octree based descriptors as input for 3D pairwise point cloud matching. The classic pipeline of 3D point cloud registration involves two steps, viz., the point feature matching and the globally consistent refinement. We focus on the first step that can be further divided into three parts, viz., the key point detection, feature descriptor extraction, and pairwise-point correspondence estimation. In practical applications, the point feature matching is ambiguous and challenging owing to the low overlap of multiple scans, inconsistency of point density, and unstructured properties. To solve these issues, we propose the KdO-Net for 3D pairwise point feature matching and present a novel nearest neighbor searching strategy to address the computation problem. Thereafter, our method is evaluated with respect to an indoor BundleFusion benchmark, and generalized to a challenging outdoor ETH dataset. Further, we have extended our method over our complicated and low-overlapped TUM-lab dataset. The empirical results graphically demonstrate that our method achieves a superior precision and a comparable feature matching recall to the prior state-of-the-art deep learning-based methods, despite the overlap being less than 30 percent. Finally, we implement quantitative and qualitative ablated experiments and visualization interpretations for illustrating the insights and behavior of our network.

Vibration monitoring is a frequent task within the general topic of Structural Health Monitoring. For this monitoring, usually accelerometers, strain gauges, fibre optic sensors or Global Navigation Satellite System (GNSS) receivers are placed on pre-selected positions on the structure and the point-wise measurements are individually processed to estimate the relevant modal parameters, for example, oscillating amplitudes and natural frequencies. If laser scanners were used for vibration monitoring, the analyses could be performed with a significantly higher spatial resolution that would be beneficial especially for locating structural weaknesses. However, to apply laser scanners rigorously to vibration monitoring, spatio-temporal models need to be set up. With this study, we develop and discuss four spatio-temporal models applied to the simulated vibration monitoring of a bridge deck. Therefore, we formulate either functional as well as stochastic connections between neighbored measurement positions within the estimation of the parameters of a harmonic oscillation. We reveal that those models allow an improved parameter estimation compared to the usually used strategies—even at lower measurement frequencies and shorter observation lengths.

This article investigates the usage of terrestrial laser scanner (TLS) point clouds for monitoring the gradual movements of soil masses due to freeze–thaw activity and water saturation, commonly referred to as solifluction. Solifluction is a geomorphic process which is characteristic for hillslopes in (high-)mountain areas, primarily alpine periglacial areas and the arctic. The movement can reach millimetre-to-centimetre per year velocities, remaining well below the typical displacement mangitudes of other frequently monitored natural objects, such as landslides and glaciers. Hence, a better understanding of solifluction processes requires increased spatial and temporal resolution with relatively high measurement accuracy. To that end, we developed a workflow for TLS point cloud processing, providing a 3D vector field that can capture soil mass displacement due to solifluction with high fidelity. This is based on the common image-processing techniques of feature detection and tracking. The developed workflow is tested on a study area placed in Hohe Tauern range of the Austrian Alps with a prominent assemblage of solifluction lobes. The derived displacements were compared with the established geomonitoring approach with total station and signalized markers and point cloud deformation monitoring approaches. The comparison indicated that the achieved results were in the same accuracy range as the established methods, with an advantage of notably higher spatial resolution. This improvement allowed for new insights considering the solifluction processes.

Mobile laser scanning has become an established measuring technique that is used for many applications in the fields of mapping, inventory, and monitoring. Due to the increasing operationality of such systems, quality control w.r.t. calibration and evaluation of the systems becomes more and more important and is subject to on-going research. This paper contributes to this topic by using tools from geodetic configuration analysis in order to design and evaluate a plane-based calibration field for determining the lever arm and boresight angles of a 2D laser scanner w.r.t. a GNSS/IMU unit (Global Navigation Satellite System, Inertial Measurement Unit). In this regard, the impact of random, systematic, and gross observation errors on the calibration is analyzed leading to a plane setup that provides accurate and controlled calibration parameters. The designed plane setup is realized in the form of a permanently installed calibration field. The applicability of the calibration field is tested with a real mobile laser scanning system by frequently repeating the calibration. Empirical standard deviations of ∘ for the boresight angles are obtained, which was priorly defined to be the goal of the calibration. In order to independently evaluate the mobile laser scanning system after calibration, an evaluation environment is realized consisting of a network of control points as well as TLS (Terrestrial Laser Scanning) reference point clouds. Based on the control points, both the horizontal and vertical accuracy of the system is found to be < 10 mm (root mean square error). This is confirmed by comparisons to the TLS reference point clouds indicating a well calibrated system. Both the calibration field and the evaluation environment are permanently installed and can be used for arbitrary mobile laser scanning systems.

Target-based calibration is the most widely used calibration procedure for terrestrial laser scanners. Nevertheless, the current implementations of this procedure are suboptimal due to the high complexity of their calibration fields. Therefore, designing calibration fields, by selecting optimal target locations with respect to the instrument, is a pending research question. Answering this question would lead to a more efficient and user-friendly calibration, as well as improved calibration results. Herein, we conduct a detailed analysis of the most sensitive target-to-instrument measurement configuration to estimate individual calibration parameters. Based on this analysis, we derive a minimal measurement geometry, sensitive to all mechanical misalignments, relevant for high-end panoramic terrestrial laser scanners. We support our sensitivity analysis by a simulation and empirical experiments followed by a discussion about the advantages and shortcomings of the proposed minimal measurement geometry.

During the registration and georeferencing of terrestrial laser scans, it is common to use targets to mark discrete points. To improve the accuracy of the registration, the uncertainties of the target center estimation (TCE) have to be minimized. The present study examines different factors influencing the precision of the TCE. Here, the focus is on the algorithm and the target design. It is determined that, in general, the uncertainties of the TCE are much smaller than those indicated by the manufacturers. By comparing different algorithms for the first time, it was possible to clearly determine that an algorithm using image correlations yields the smallest standard deviations for the TCE. A comparison of different target designs could not identify an ideal commercially available target. For this reason, a new target, the BOTA8 (BOnn TArget with 8-fold pattern) was developed, which leads to smaller standard deviations than the previous targets. By choosing the best algorithm and improving the target design, standard deviations of 0.5 mm in distance direction and 1.2 arcsec in angular direction for a scan distance up to 100 m were achieved with the laser scanner Leica ScanStation P20. The uncertainties could be reduced by several millimetres and angular seconds compared to the manufacturer’s targets and software.

As laser scanning technology has improved a lot in recent years, terrestrial laser scanners (TLS) have become popular devices for surveying tasks with high accuracy demands, such as deformation analyses. For this reason, finding a stochastic model for TLS measurements is very important in order to get statistically reliable results. The measurement accuracy of laser scanners—especially of their rangefinders—is strongly dependent on the scanning conditions, such as the scan configuration, the object surface geometry and the object reflectivity. This study demonstrates a way to determine the intensity-dependent range precision of 3D points for terrestrial laser scanners that measure in 3D mode by using range residuals in laser beam direction of a best plane fit. This method does not require special targets or surfaces aligned perpendicular to the scanner, which allows a much quicker and easier determination of the stochastic properties of the rangefinder. Furthermore, the different intensity types—raw and scaled—intensities are investigated since some manufacturers only provide scaled intensities. It is demonstrated that the intensity function can be derived from raw intensity values as written in literature, and likewise—in a restricted measurement volume—from scaled intensity values if the raw intensities are not available.

Kinematic laser scanning with moving platforms has been used for the acquisition of 3D point clouds of our environment for many years. A main application of these mobile systems is the acquisition of the infrastructure, e.g., the road surface and buildings. Regarding this, the distance between laser scanner and object is often notably shorter than 20 m. In the close range, however, divergent incident laser light can lead to a deterioration of the precision of laser scanner distance measurements. In the light of this, we analyze the distance precision of the 2D laser scanner Z + F Profiler 9012A, purpose-built for kinematic applications, in the range of up to 20 m. In accordance with previous studies, a clear dependency between scan rate, intensity of the backscattered laser light and distance precision is evident, which is used to derive intensity-based stochastic models for the sensor. For this purpose, a new approach for 2D laser scanners is proposed that is based on the static scanning of surfaces with different backscatter. The approach is beneficial because the 2D laser scanner is operated in its normal measurement mode, no sophisticated equipment is required and no model assumptions for the scanned surface are made. The analysis reveals a lower precision in the range below 5 m caused by a decreased intensity. However, the Z + F Profiler 9012A is equipped with a special hardware-based close range optimization partially compensating for this. Our investigations show that this optimization works best at a distance of about 2 m. Although increased noise remains a critical factor in the close range, the derived stochastic models are also valid below 5 m.

For accurate astronomic and geodetic observations based on radio telescopes, the elevation-dependent deformation of the radio telescopes’ main reflectors should be known. Terrestrial laser scanning has been used for determining the corresponding changes of focal lengths and areal reflector deformations at several occasions before. New in this publication is the situation in which we minimize systematic measurement errors by an improved measurement and data-processing concept: Sampling the main reflector in both faces of the laser scanner and calibrating the laser scanner in situ in a bundle adjustment. This concept is applied to the Onsala Space Observatory 20-m radio telescope: The focal length of the main reflector decreases by 9.6 mm from 85 ∘ to 5 ∘ elevation angle. Further local deformations of the main reflector are not detected.

Measuring the 3D shape of plants for phenotyping purposes using active 3D laser scanning devices has become an important field of research. While the acquisition of stem and root structure is mostly straightforward, extensive and non-invasive measuring of the volumetric shape of leaves, i.e., the leaf thickness, is more challenging. Therefore, the purpose of this paper is to examine whether the leaf thickness is measurable using a high precision industrial laser scanning system. The study comprises a metrological investigation of the accuracy of the laser scanning system with regards to thickness measurements as well as experiments for leaf thickness measurements using several leaves of three different types of crop. The results indicate that although the measuring system is principally able to measure thicknesses of about 74 μ m with statistical certainty, the leaf thickness is not measurable accurately. The reason for this can be attributed to the measurable penetration depth of the laser scanner combined with the variation of the angle of incidence. These effects cause systematic uncertainties and significant variations of the derived leaf thickness.