Performance Index for Extrinsic Calibration of LiDAR and Motion Sensor for Mapping and Localization

Light Detection and Ranging (LiDAR) is a sensor that uses a laser to represent the surrounding environment in three-dimensional information. Thanks to the development of LiDAR, LiDAR-based applications are being actively used in autonomous vehicles. In order to effectively use the information coming from LiDAR, extrinsic calibration which finds the translation and the rotation relationship between LiDAR coordinate and vehicle coordinate is essential. Therefore, many studies on LiDAR extrinsic calibration are steadily in progress. The performance index (PI) of the calibration parameter is a value that quantitatively indicates whether the obtained calibration parameter is similar to the true value or not. In order to effectively use the obtained calibration parameter, it is important to validate the parameter through PI. Therefore, in this paper, we propose an algorithm to obtain the performance index for the calibration parameter between LiDAR and the motion sensor. This performance index is experimentally verified in various environments by Monte Carlo simulation and validated using CarMaker simulation data and real data. As a result of verification, the PI of the calibration parameter obtained through the proposed algorithm has the smallest value when the calibration parameter has a true value, and increases as an error is added to the true value. In other words, it has been proven that PI is convex to the calibration parameter. In addition, it is able to confirm that the PI obtained using the proposed algorithm provides information on the effect of the calibration parameters on mapping and localization.

Improved calibration of the Green–Ampt infiltration module in the EROSION-2D/3D model using a rainfall-runoff experiment database

Soil infiltration is one of the key factors that has an influence on soil erosion caused by rainfall. Therefore, a well-represented infiltration process is a necessary precondition for successful soil erosion modelling. Complex natural conditions do not allow the full mathematical description of the infiltration process, and additional calibration parameters are required. The Green–Ampt-based infiltration module in the EROSION-2D/3D model introduces a calibration parameter “skinfactor” to adjust saturated hydraulic conductivity. Previous studies provide skinfactor values for several combinations of soil and vegetation conditions. However, their accuracies are questionable, and estimating the skinfactors for other than the measured conditions yields significant uncertainties in the model results. This study brings together an extensive database of rainfall simulation experiments, the state-of-the-art model parametrisation method and linear mixed-effect models to statistically analyse relationships between soil and vegetation conditions and the model calibration parameter skinfactor. New empirically based transfer functions for skinfactor estimation significantly improving the accuracy of the infiltration module and thus the overall EROSION-2D/3D model performance are provided in this study. Soil moisture and bulk density were identified as the most significant predictors explaining 82 % of the skinfactor variability, followed by the soil texture, vegetation cover and impact of previous rainfall events. The median absolute percentage error of the skinfactor prediction was improved from 71 % using the currently available method to 30 %–34 % using the presented transfer functions, which led to significant decrease in error propagation into the model results compared to the present method. The strong logarithmic relationship observed between the calibration parameter and soil moisture however indicates high overestimation of infiltration for dry soils by the algorithms implemented in EROSION-2D/3D and puts the state-of-the-art parametrisation method in question. An alternative parameter optimisation method including calibration of two Green–Ampt parameters' saturated hydraulic conductivity and water potential at the wetting front was tested and compared with the state-of-the-art method, which paves a new direction for future EROSION-2D/3D model parametrisation.

Continuous calibration of a digital twin: Comparison of particle filter and Bayesian calibration approaches

Assimilation of continuously streamed monitored data is an essential component of a digital twin; the assimilated data are used to ensure the digital twin represents the monitored system as accurately as possible. One way this is achieved is by calibration of simulation models, whether data-derived or physics-based, or a combination of both. Traditional manual calibration is not possible in this context; hence, new methods are required for continuous calibration. In this paper, a particle filter methodology for continuous calibration of the physics-based model element of a digital twin is presented and applied to an example of an underground farm. The methodology is applied to a synthetic problem with known calibration parameter values prior to being used in conjunction with monitored data. The proposed methodology is compared against static and sequential Bayesian calibration approaches and compares favourably in terms of determination of the distribution of parameter values and analysis run times, both essential requirements. The methodology is shown to be potentially useful as a means to ensure continuing model fidelity.

Influence of Calibration Parameter Selection on Flash Flood Simulation for Small to Medium Catchments with MISDc-2L Model

It is of great challenge to accurately predict flash floods for small to medium catchments (SMC) in mountainous areas, for which parameter calibration strategies are crucial for model performance. This study investigates the influence of calibration parameter selection on flash flood simulations using a rainfall–runoff model, MISDc-2L (Modello Idrologico Semi-Distribuito in continuo–2 layers), at hourly scale for SMC in the Huai River basin of China over the 2010–2015 period. We investigated model performances under different calibration schemes, where different amounts of model parameters were selected for the calibration procedure. The model clearly performed better in the case involving calibration of partial sensitive parameters than that of a full parameter set with respect to the peaks, the hydrographs and the base-flow of flood simulation, especially after including maximum water capacity (W_max) in the calibration. This finding was consistently valid under different model calibration experiments, including single event, “split-sample” test and combined events at different flood magnitude levels. We further found that the model performed better for high magnitude flood events than medium and low ones, but clear improvements can be achieved for low and medium magnitude flood events with careful calibration parameter selection. Our study suggests that calibration parameter selection is important for flash flood event simulations with the MISDc-2L model for SMC in the Huai River basin of China; specifically, the reduction in calibration parameter amount and the inclusion of W_max in calibration remarkably improve flood simulation.

Instrument Radiometric Processing for Earh Observation Satellite Optical Payload

The radiometric calibration procedure, applied on VNREDSat-1 raw data shall correct and minimize the radiometric non-uniform gain and offset, occurred by the nature of the optical payload. The procedure is based on the radiometric model, built to transform the incoming radiance into camera output signal. Within this radiometric model, the radiometric parameters such as dark noise and pixel response non-uniformity shall be separated. The output signal of dark noise shall be measured by capturing image scene of the ocean at night. The output signal of pixel response non-uniformity shall be measured by capturing image scene of uniform radiance image scene such as the dessert or grass field. All the radiometric calibration parameter after calculated shall be saved into a calibration file, which is kept on the control station and image processing station.

Asteroseismology of evolved stars to constrain the internal transport of angular momentum

Context. Asteroseismic observations enable the characterisation of the internal rotation of evolved stars. These measurements reveal that an unknown efficient angular momentum (AM) transport mechanism is needed for subgiant and red giant stars in addition to hydrodynamic transport processes. A revised prescription for AM transport by the magnetic Tayler instability has been recently proposed as a possible candidate for such a missing mechanism. Aims. We compare the rotational properties predicted by this magnetic AM transport to asteroseismic constraints obtained for evolved stars with a particular focus on the subgiant phase. Methods. We computed models accounting for the recent prescription for AM transport by the Tayler instability with the Geneva stellar evolution code for subgiant and red giant stars, for which an asteroseismic determination of both core and surface rotation rates is available. Results. The revised prescription for the transport by the Tayler instability leads to low core rotation rates after the main sequence that are in better global agreement with asteroseismic measurements than those predicted by models with purely hydrodynamic processes or with the original Tayler-Spruit dynamo. A detailed comparison with asteroseismic data shows that the rotational properties of at most two of the six subgiants can be correctly reproduced by models accounting for this revised magnetic transport process. This result is obtained independently of the value adopted for the calibration parameter in this prescription. We also find that this transport by the Tayler instability faces difficulties in simultaneously reproducing asteroseismic measurements available for subgiant and red giant stars. The low values of the calibration parameter needed to correctly reproduce the rotational properties of two of the six subgiants lead to core rotation rates during the red giant phase that are too high. Inversely, the higher values of this parameter needed to reproduce the core rotation rates of red giants lead to a very low degree of radial differential rotation before the red giant phase, which is in contradiction with the internal rotation of subgiant stars. Conclusions. In its present form, the revised prescription for the transport by the Tayler instability does not provide a complete solution to the missing AM transport revealed by asteroseismology of evolved stars.

Least Bumpiness Calibration With Extrapolative Bias Correction

Bias correction is important for model calibration to obtain unbiased calibration parameter estimates and make accurate prediction. However, calibration often relies on insufficient samples, and so bias correction often mostly depends on extrapolation. For example, bias correction with twelve samples in nine-dimensional box generated by Latin Hypercube Sampling (LHS) has less than 0.1% interpolation domain in the box. Since bias correction is coupled with calibration parameter estimation, calibration with extrapolative bias correction can lead a large error in the calibrated parameters. This paper proposes an idea of calibration with minimum bumpiness correction. The bumpiness of bias correction is a good measure of assessing the potential risk of a large error in the correction. By minimizing bumpiness, the risk of extrapolation can be reduced while the accuracy of parameter estimates can be achieved. It was found that this calibration method gave more accurate results than Bayesian calibration for an analytical example. It was also found that there are common denominators between the proposed method and the Bayesian calibration with bias correction.