Feasibility of Consumer Grade GNSS Receivers for the Integration in Multi-Sensor-Systems

Various GNSS applications require low-cost, small-scale, lightweight and power-saving GNSS devices and require high precision in terms of low noise for carrier phase and code observations. Applications vary from navigation approaches to positioning in geo-monitoring units up to integration in multi-sensor-systems. For highest precision, only GNSS receivers are suitable that provide access to raw data such as carrier phase, code ranges, Doppler and signal strength. A system integration is only possible if the overall noise level is known and quantified at the level of the original observations. A benchmark analysis based on a zero baseline is proposed to quantify the stochastic properties. The performance of the consumer grade GNSS receiver is determined and evaluated against geodetic GNSS receivers to better understand the utilization of consumer grade receivers. Results indicate high similarity to the geodetic receiver, even though technical limitations are present. Various stochastic techniques report normally distributed carrier-phase noise of 2mm and code-range noise of 0.5-0.8m. This is confirmed by studying the modified Allan standard deviation and code-minus-carrier combinations. Derived parameters serve as important indicators for the integration of GNSS receivers into multi-sensor-systems

A fully automated three-stage procedure for spatio-temporal leaf segmentation with regard to the B-spline-based phenotyping of cucumber plants

Plant phenotyping deals with the metrological acquisition of plants in order to investigate the impact of environmental factors and a plant’s genotype on its appearance. Phenotyping methods that are used as standard in crop science are often invasive or even destructive. Due to the increase of automation within geodetic measurement systems and with the development of quasi-continuous measurement techniques, geodetic techniques are perfectly suitable for performing automated and non-invasive phenotyping and, hence, are an alternative to standard phenotyping methods. In this contribution, sequentially acquired point clouds of cucumber plants are used to determine the plants’ phenotypes in terms of their leaf areas. The focus of this contribution is on the spatio-temporal segmentation of the acquired point clouds, which automatically groups and tracks those sub point clouds that describe the same leaf. The application on example data sets reveals a successful segmentation of 93% of the leafs. Afterwards, the segmented leaves are approximated by means of B-spline surfaces, which provide the basis for the subsequent determination of the leaf areas. In order to validate the results, the determined leaf areas are compared to results obtained by means of standard methods used in crop science. The investigations reveal consistency of the results with maximal deviations in the determined leaf areas of up to 5

Direct geo-referencing of a static terrestrial laser scanner

This paper describes an adaptive extended Kalman filter (AEKF) approach for geo-referencing tasks for a multi-sensor system (MSS). The MSS is a sensor fusion of a phase-measuring terrestrial laser scanner (TLS) with navigation sensors such as Global Navigation Satellite System (GNSS) equipment and inclinometers. The position and orientation of the MSS are the main parameters which are constant on a station and will be derived by a Kalman filtering process. Hence, the orientation of a TLS/MSS can be done without any demand for other artificial targets in the scanning area. However, using inclinometer measurements the spatial rotation angles about the X- and Y-axis of the fixed MSS station can be estimated by the AEKF. This makes it possible to determine all six degrees of freedom of the transformation from a sensor-defined to a global coordinate system. The paper gives a detailed discussion of the strategy used for the direct geo-referencing. The AEKF for the transformation parameters estimation is presented with focus on the modelling of the MSS motion. The usefulness of the suggested approach will be demonstrated using practical investigations

High-precision 3D object capturing with static and kinematic terrestrial laser scanning in industrial applications-approaches of quality assessment

Abstract 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

How significant are differences obtained by neglecting correlations when testing for deformation: A real case study using bootstrapping with terrestrial laser scanner observations approximated by b-spline surfaces

B-spline surfaces possess attractive properties such as a high degree of continuity or the local support of their basis functions. One of the major applications of B-spline surfaces in engineering geodesy is the least-square (LS) fitting of surfaces from, e.g., 3D point clouds obtained from terrestrial laser scanners (TLS). Such mathematical approximations allow one to test rigorously with a given significance level the deformation magnitude between point clouds taken at different epochs. Indeed, statistical tests cannot be applied when point clouds are processed in commonly used software such as CloudCompare, which restrict the analysis of deformation to simple deformation maps based on distance computation. For a trustworthy test decision and a resulting risk management, the stochastic model of the underlying observations needs, however, to be optimally specified. Since B-spline surface approximations necessitate Cartesian coordinates of the TLS observations, the diagonal variance covariance matrix (VCM) of the raw TLS measurements has to be transformed by means of the error propagation law. Unfortunately, this procedure induces mathematical correlations, which can strongly affect the chosen test statistics to analyse deformation, if neglected. This may lead potentially to rejecting wrongly the null hypothesis of no-deformation, with risky and expensive consequences. In this contribution, we propose to investigate the impact of mathematical correlations on test statistics, using real TLS observations from a bridge under load. As besides TLS, a highly precise laser tracker (LT) was used, the significance of the difference of the test statistics when the stochastic model is misspecified can be assessed. However, the underlying test distribution is hardly tractable so that only an adapted bootstrapping allows the computation of trustworthy p-values. Consecutively, the extent to which heteroscedasticity and mathematical correlations can be neglected or simplified without impacting the test decision is shown in a rigorous way, paving the way for a simplification based on the intensity model

Guiding Deep Learning with Expert Knowledge for Dense Stereo Matching

Dense depth information can be reconstructed from stereo images using conventional hand-crafted as well as deep learning-based approaches. While deep-learning methods often show superior results compared to hand-crafted ones, they commonly learn geometric principles underlying the matching task from scratch and neglect that these principles have already been intensively studied and were considered explicitly in various models with great success in the past. In consequence, a broad range of principles and associated features need to be learned, limiting the possibility to focus on important details to also succeed in challenging image regions, such as close to depth discontinuities, thin objects and in weakly textured areas. To overcome this limitation, in this work, a hybrid technique, i.e., a combination of conventional hand-crafted and deep learning-based methods, is presented, addressing the task of dense stereo matching. More precisely, the input RGB stereo images are supplemented by a fourth image channel containing feature information obtained with a method based on expert knowledge. In addition, the assumption that edges in an image and discontinuities in the corresponding depth map coincide is modeled explicitly, allowing to predict the probability of being located next to a depth discontinuity per pixel. This information is used to guide the matching process and helps to sharpen correct depth discontinuities and to avoid the false prediction of such discontinuities, especially in weakly textured areas. The performance of the proposed method is investigated on three different data sets, including studies on the influence of the two methodological components as well as on the generalization capability. The results demonstrate that the presented hybrid approach can help to mitigate common limitations of deep learning-based methods and improves the quality of the estimated depth maps

Derivation of Soil Roughness Using Multi-Temporal Laser Scanning Point Clouds

The present paper deals with the derivation of roughness parameters of soil from laser scanning point clouds, allowing to draw conclusions about properties of the soil (e. g. soil aggregation, soil sealing) and related to this its vulnerability to soil erosion. For this purpose, soil erosion scenes with agricultural cultivation are acquired in five measuring epochs under field conditions. For geo-referencing of the acquired time series of point clouds, a target-based and a data-driven approach are compared. In order to derive roughness parameters, the point clouds are cleaned of vegetation and then modelled to reduce the measurement noise. For this purpose, a collocation-based approach is used, in which a trend surface roughly approximates the point clouds. Remaining systematic deviations, which are caused by the insufficient functional approximation, are caught with the help of a stochastic signal, the variances and correlations lengths of which simultaneously provide information about the soil’s roughness

Adjustment models for multivariate geodetic time series with vector-autoregressive errors

In this contribution, a vector-autoregressive (VAR) process with multivariate t-distributed random deviations is incorporated into the Gauss-Helmert model (GHM), resulting in an innovative adjustment model. This model is versatile since it allows for a wide range of functional models, unknown forms of auto- and cross-correlations, and outlier patterns. Subsequently, a computationally convenient iteratively reweighted least squares method based on an expectation maximization algorithm is derived in order to estimate the parameters of the functional model, the unknown coefficients of the VAR process, the cofactor matrix, and the degree of freedom of the t-distribution. The proposed method is validated in terms of its estimation bias and convergence behavior by means of a Monte Carlo simulation based on a GHM of a circle in two dimensions. The methodology is applied in two different fields of application within engineering geodesy: In the first scenario, the offset and linear drift of a noisy accelerometer are estimated based on a Gauss-Markov model with VAR and multivariate t-distributed errors, as a special case of the proposed GHM. In the second scenario real laser tracker measurements with outliers are adjusted to estimate the parameters of a sphere employing the proposed GHM with VAR and multivariate t-distributed errors. For both scenarios the estimated parameters of the fitted VAR model and multivariate t-distribution are analyzed for evidence of auto- or cross-correlations and deviation from a normal distribution regarding the measurement noise

Comparative Analysis of Algorithms to Cleanse Soil Micro-Relief Point Clouds

Detecting changes in soil micro-relief in farmland helps to understand degradation processes like sheet erosion. Using the high-resolution technique of terrestrial laser scanning (TLS), we generated point clouds of three 2 × 3 m plots on a weekly basis from May to mid-June in 2022 on cultivated farmland in Germany. Three well-known applications for eliminating vegetation points in the generated point cloud were tested: Cloth Simulation Filter (CSF) as a filtering method, three variants of CANUPO as a machine learning method, and ArcGIS PointCNN as a deep learning method, a sub-category of machine learning using deep neural networks. We assessed the methods with hard criteria such as F1 score, balanced accuracy, height differences, and their standard deviations to the reference surface, resulting in data gaps and robustness, and with soft criteria such as time-saving capacity, accessibility, and user knowledge. All algorithms showed a low performance at the initial measurement epoch, increasing with later epochs. While most of the results demonstrate a better performance of ArcGIS PointCNN, this algorithm revealed an exceptionally low performance in plot 1, which is describable by the generalization gap. Although CANUPO variants created the highest amount of data gaps, we recommend that CANUPO include colour values in combination with CSF

Integrated GNSS Attitude Determination and Positioning for Direct Geo-Referencing

Direct geo-referencing is an efficient methodology for the fast acquisition of 3D spatial data. It requires the fusion of spatial data acquisition sensors with navigation sensors, such as Global Navigation Satellite System (GNSS) receivers. In this contribution, we consider an integrated GNSS navigation system to provide estimates of the position and attitude (orientation) of a 3D laser scanner. The proposed multi-sensor system (MSS) consists of multiple GNSS antennas rigidly mounted on the frame of a rotating laser scanner and a reference GNSS station with known coordinates. Precise GNSS navigation requires the resolution of the carrier phase ambiguities. The proposed method uses the multivariate constrained integer least-squares (MC-LAMBDA) method for the estimation of rotating frame ambiguities and attitude angles. MC-LAMBDA makes use of the known antenna geometry to strengthen the underlying attitude model and, hence, to enhance the reliability of rotating frame ambiguity resolution and attitude determination. The reliable estimation of rotating frame ambiguities is consequently utilized to enhance the relative positioning of the rotating frame with respect to the reference station. This integrated (array-aided) method improves ambiguity resolution, as well as positioning accuracy between the rotating frame and the reference station. Numerical analyses of GNSS data from a real-data campaign confirm the improved performance of the proposed method over the existing method. In particular, the integrated method yields reliable ambiguity resolution and reduces position standard deviation by a factor of about 0.8, matching the theoretical gain of √3/4 for two antennas on the rotating frame and a single antenna at the reference station