An Investigation on a Plane-Based Dynamic Calibration Method for the Handheld LiDAR Scanner

A plane-based dynamic calibration method had been proposed by the previous study for the GeoSLAM ZEB Horizon handheld LiDAR scanner. Only one preliminary test was presented. Therefore, three datasets in a calibration field were collected in this study on different dates and at different times on the same date to investigate the efficiency of the proposed calibration approach and calibration results. The calibration results for these three datasets showed that all average residuals were closer to 0, and all a posterior unit weight standard deviations of the adjustment were also significantly reduced after calibration. Moreover, the RMSE (root mean square error) of the check planes was improved by about an average of 32.61%, 28.44%, and 14.7%, respectively, for the three datasets. The improvement was highly correlated with the quality of the calibration data. The RMSE differences of all check planes using calibration data collected on different dates and at different times on the same date for calibration was about 1–2 cm and less than 1 mm, respectively. There was no difference in the calibration results, demonstrating the efficiency of the proposed calibration approach and the calibration results during the two different dates.

Spatio-temporally Varying Manning Roughness in Rivers and Streams: A calibration approach using in-situ water level and UAS altimetry

Hydraulic roughness (expressed in terms of e.g. Manning's roughness coefficient) is an important input to hydraulic and hydrodynamic simulation models. One way to estimate roughness parameters is by hydraulic inversion, using observed water surface elevation (WSE) collected from gauging stations, satellite platforms or UAS (Unmanned Aerial System) −based altimeters. Specifically, UAS altimetry provides close to instantaneous observations of longitudinal profiles and seasonal variations of WSE for various river types, which are useful for calibrating roughness parameters. However, it is computationally expensive to run high−resolution hydrodynamic models for long simulation periods (e.g. multiple years), and thus global optimization of spatially and temporally distributed parameter sets for such models, e.g., spatio−temporally varying river roughness, is still challenging.This study presented an efficient calibration approach for hydraulic models, using a simplified steady-state hydraulic solver, UAS altimetry datasets, and in-situ observations. The calibration approach minimized the weighted sum of a misfit term, spatial smoothness penalty, and a sinusoidal a priori temporal variation constraint. The approach was first demonstrated for several synthetic calibration experiments and the results indicated that the global search algorithm accurately recovered the Manning–Strickler coefficients M for short river reaches in different seasons, and M varied significantly in time (due to the seasonal growth cycle of the aquatic vegetation) and space (due to, e.g. spatially variable vegetation density). Subsequently, the calibration approach was demonstrated for a real WSE dataset collected at a Danish test site, i.e., Vejle Å. Results indicated that spatio-temporal variation in M was required to accurately fit in-situ and UAS altimetry WSE observations. This study illustrated how UAS altimetry and hydraulic modeling can be combined to achieve improved understanding and better parameterization of small and medium-sized rivers, where conveyance is controlled by vegetation growth and other spatio-temporally variable factors.

Some Model Assisted Estimators Using Functional Form Calibration Approach

The Model assisted estimators are approximately design unbiased, consistent and provides robustness in the case of large sample sizes. The model assisted estimators result in reduction of the design variance if underlying model reasonably defines the regression relationship. If the model is misspecified, then model assisted estimators might result in an increase of the design variance but remain approximately design unbiased and show robustness against model-misspecification. The well-known model assisted estimators, generalized regression estimators are members of a larger class of calibration estimators. Calibration method generates calibration weights that meet the calibration constraints and have minimum distance from the sampling design weights. By using different distance measures, classical calibration approach generates different calibration estimators but with asymptotically identical properties. The constraint of distance minimization was reduced for studying the properties of calibration estimators by proposing a simple functional form approach. The approach generates calibration weights that prove helpful to control the changes in calibration weights by using different choices of auxiliary variable’s functions. This paper is an extended work on model assisted approach by using functional form of calibration weights. Some new model assisted estimators are considered to get efficient and stabilized regression weights by introducing a control matrix. The asymptotic un-biasedness of the proposed estimators is verified and the expressions for MSE are derived in three different cases. A simulation study is done to compare and evaluate the efficiency of the proposed estimators with some existing model assisted estimators.

A geometric calibration approach for industrial cone-beam CT system based on low-rank phantom

Precise alignment of the system scan geometry is crucial to ensure the reconstructed image quality in the cone-beam CT system. A calibration method that depends on the local feature of ball bearings phantom and point-like markers is probably affected by local image variations. Besides, multiple projections with circular scanning are usually required by this type of method to derive misaligned parameters. In contrast to previous works, this paper proposes a method that depends on the global symmetric low-rank feature of a novel phantom, which can accurately represent the system geometrical misalignment. All the misaligned parameters of the cone-beam CT system can be estimated from a single perspective direction without circular scanning. Meanwhile, since the global low-rank feature of the phantom is utilized, the proposed method is robust to the noise. Extensive simulations and real experiments validate the accuracy and robustness of our method, which achieves better performance compared to an existing phantom-based method.

The influence of the Earth’s magnetic field on strapdown inertial gravimetry using Q-Flex accelerometers: static and dynamic experiments

In recent strapdown airborne and shipborne gravimetry campaigns with servo accelerometers of the widely used Q-Flex type, results have been impaired by heading-dependent measurement errors. This paper shows that the effect is, in all likelihood, caused by the sensitivity of the Q-Flex type sensor to the Earth’s magnetic field. In order to assess the influence of magnetic fields on the utilised strapdown IMU of the type iMAR iNAV-RQH-1003, the IMU has been exposed to various magnetic fields of known directions and intensities in a 3-D Helmholtz coil. Based on the results, a calibration function for the vertical accelerometer is developed. At the example of five shipborne and airborne campaigns, it is outlined that under specific circumstances the precision of the gravimetry results can be strongly improved using the magnetic calibration approach: The non-adjusted RMSE at repeated lines decreased from 1.19 to 0.26 mGal at a shipborne campaign at Lake Müritz, Germany. To the knowledge of the authors, a significant influence of the Earth’s magnetic field on strapdown inertial gravimetry is demonstrated for the first time.