scholarly journals A 3D Compensation Method for the Systematic Errors of Kinect V2

2021 ◽  
Vol 13 (22) ◽  
pp. 4583
Author(s):  
Chang Li ◽  
Bingrui Li ◽  
Sisi Zhao
Keyword(s):  
Systematic Error ◽  
Iteration Method ◽  
Measurement Accuracy ◽  
Systematic Errors ◽  
Compensation Method ◽  
Model Errors ◽  
3D Registration ◽  
Kinect V2 ◽  
Depth Data ◽  
Variable Weights

To reduce the 3D systematic error of the RGB-D camera and improve the measurement accuracy, this paper is the first to propose a new 3D compensation method for the systematic error of a Kinect V2 in a 3D calibration field. The processing of the method is as follows. First, the coordinate system between the RGB-D camera and 3D calibration field is transformed using 3D corresponding points. Second, the inliers are obtained using the Bayes SAmple Consensus (BaySAC) algorithm to eliminate gross errors (i.e., outliers). Third, the parameters of the 3D registration model are calculated by the iteration method with variable weights that can further control the error. Fourth, three systematic error compensation models are established and solved by the stepwise regression method. Finally, the optimal model is selected to calibrate the RGB-D camera. The experimental results show the following: (1) the BaySAC algorithm can effectively eliminate gross errors; (2) the iteration method with variable weights could better control slightly larger accidental errors; and (3) the 3D compensation method can compensate 91.19% and 61.58% of the systematic error of the RGB-D camera in the depth and 3D directions, respectively, in the 3D control field, which is superior to the 2D compensation method. The proposed method can control three types of errors (i.e., gross errors, accidental errors and systematic errors) and model errors and can effectively improve the accuracy of depth data.

scholarly journals STUDI PERAMBATAN KESALAHAN SISTEMATIS LUAS LAHAN PARKIR KAMPUS MENGGUNAKAN PENDEKATAN LUAS SEGITIGA SEMBARANG

2019 ◽  
Vol 13 (1) ◽  
pp. 14
Author(s):  
Hendro Supratikno ◽  
David Premana
Keyword(s):  
Systematic Error ◽  
Error Propagation ◽  
Measurement Data ◽  
Systematic Errors ◽  
Random Errors ◽  
Land Area ◽  
Parking Lots ◽  
Parking Lot ◽  
Data Error ◽  
Definition Of

Parking is a condition of not moving a vehicle that is temporary because it was abandoned by the driver. Included in the definition of parking is every vehicle that stops at certain places whether stated by traffic signs or not, and not solely for the benefit of raising and / or lowering people and / or goods.Campus 3 Lumajang State Community Academy has facilities and infrastructure prepared by the Lumajang Regency government. However, the parking lots provided cannot accommodate vehicles optimally because of the ratio of the number of vehicles and the area of the parking area that is not appropriate. This is because the area of the parking lot is not analyzed by data error when measuring.Each measurement data is assumed to have errors both systematic errors, random errors, and large errors (blunders), so that in the measurement of parking lots certainly there are errors. From this the authors intend to conduct research to find out how the propagation of systematic errors and the large systematic errors of the area of campus parking lot 3 Lumajang Community Academy.The methods used in this study include preparing materials and tools, making land sketches, decomposing them, determining distances using theodolite, determining land area equations, and finding systematic error propagation. So that the final goal in this study is to find large systematic errors in the parking area of Campus 3 of the Lumajang State Community Academy

Error sources and models of the laser tracker without a beam steering mirror

2021 ◽  
Author(s):  
Radoslav Choleva ◽  
Alojz Kopáčik
Keyword(s):  
Measurement Accuracy ◽  
Beam Steering ◽  
Systematic Errors ◽  
Error Modeling ◽  
Laser Tracker ◽  
High Demand ◽  
Error Sources ◽  
Error Models ◽  
Measurement Results

AbstractThe laser tracker is a widely used instrument in many industrial and metrological applications with high demand measurement accuracy. Imperfections in construction and misalignment of individual parts deliver systematic errors in the measurement results. All error sources need to be identified and reduced to the minimum to achieve the best possible accuracy. The paper summarizes error sources of the laser tracker without beam steering mirror with emphasis on error modeling. Descriptions of error models are provided for the static and kinematic type of measurement.

Dynamic Measurement of Bearing Radial Clearances

2005 ◽  
Vol 295-296 ◽  
pp. 361-366 ◽  
Author(s):  
Yu Xue Chen ◽  
S.N. Yang
Keyword(s):  
Measurement Accuracy ◽  
Systematic Errors ◽  
Dynamic Measurement ◽  
Outer Ring ◽  
Radial Clearance ◽  
Test Time ◽  
Ball Bearings ◽  
Test Results ◽  
Accuracy Measures

Outer ring tilted or offset axially, caused by improperly fixing relatively to the inner ring, will produce remarkable systematic errors in measured radial clearance of a bearing. Analyzing their effects in detail on the results of measured radial clearances, they are found to be the main sources affecting the measurement accuracy. Measures for removing them are proposed. Based on these, a novel type of instrument for measurement of radial clearances of ball bearings has been developed. It could avoid the two kinds of systematic errors. The measuring principle, structures and working procedures of the instrument are presented. Test results show that the test time is less than 15 seconds per a part and the indication stability is between ±1.0 µm.

scholarly journals Towards space based verification of CO<sub>2</sub> emissions from strong localized sources: fossil fuel power plant emissions as seen by a CarbonSat constellation

2011 ◽  
Vol 4 (4) ◽  
pp. 5147-5182
Author(s):  
V. A. Velazco ◽  
M. Buchwitz ◽  
H. Bovensmann ◽  
M. Reuter ◽  
O. Schneising ◽  
...  
Keyword(s):  
Power Plant ◽  
Systematic Error ◽  
Co2 Emissions ◽  
Power Plants ◽  
Fossil Fuel ◽  
Systematic Errors ◽  
Random Errors ◽  
Power Plant Emissions ◽  
Global Coverage ◽  
Plant Emissions

Abstract. Carbon dioxide (CO2) is the most important man-made greenhouse gas (GHG) that cause global warming. With electricity generation through fossil-fuel power plants now as the economic sector with the largest source of CO2, power plant emissions monitoring has become more important than ever in the fight against global warming. In a previous study done by Bovensmann et al. (2010), random and systematic errors of power plant CO2 emissions have been quantified using a single overpass from a proposed CarbonSat instrument. In this study, we quantify errors of power plant annual emission estimates from a hypothetical CarbonSat and constellations of several CarbonSats while taking into account that power plant CO2 emissions are time-dependent. Our focus is on estimating systematic errors arising from the sparse temporal sampling as well as random errors that are primarily dependent on wind speeds. We used hourly emissions data from the US Environmental Protection Agency (EPA) combined with assimilated and re-analyzed meteorological fields from the National Centers of Environmental Prediction (NCEP). CarbonSat orbits were simulated as a sun-synchronous low-earth orbiting satellite (LEO) with an 828-km orbit height, local time ascending node (LTAN) of 13:30 (01:30 p.m.) and achieves global coverage after 5 days. We show, that despite the variability of the power plant emissions and the limited satellite overpasses, one CarbonSat can verify reported US annual CO2 emissions from large power plants (≥5 Mt CO2 yr−1) with a systematic error of less than ~4.9 % for 50 % of all the power plants. For 90 % of all the power plants, the systematic error was less than ~12.4 %. We additionally investigated two different satellite configurations using a combination of 5 CarbonSats. One achieves global coverage everyday but only samples the targets at fixed local times. The other configuration samples the targets five times at two-hour intervals approximately every 6th day but only achieves global coverage after 5 days. From the statistical analyses, we found, as expected, that the random errors improve by approximately a factor of two if 5 satellites are used. On the other hand, more satellites do not result in a large reduction of the systematic error. The systematic error is somewhat smaller for the CarbonSat constellation configuration achieving global coverage everyday. Finally, we recommend the CarbonSat constellation configuration that achieves daily global coverage.

scholarly journals Measurement for the Thickness of Water Droplets/Film on a Curved Surface with Digital Image Projection (DIP) Technique

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2409
Author(s):  
Lingwei Zeng ◽  
Hanfeng Wang ◽  
Ying Li ◽  
Xuhui He
Keyword(s):  
Theoretical Analysis ◽  
Systematic Error ◽  
Digital Image ◽  
Systematic Errors ◽  
Curved Surface ◽  
Water Droplets ◽  
Image Projection

Digital image projection (DIP) with traditional vertical calibration cannot be used for measuring the water droplets/film on a curved surface, because significant systematic error will be introduced. An improved DIP technique with normal calibration is proposed in the present paper, including the principles, operation procedures and analysis of systematic errors, which was successfully applied to measuring the water droplets/film on a curved surface. By comparing the results of laser profiler, traditional DIP, improved DIP and theoretical analysis, advantages of the present improved DIP technique are highlighted.

scholarly journals A quantitative analysis of grid-related systematic errors in oxidising capacity and ozone production rates in chemistry transport models

2004 ◽  
Vol 4 (7) ◽  
pp. 1781-1795 ◽  
Author(s):  
J. G. Esler ◽  
G. J. Roelofs ◽  
M. O. Köhler ◽  
F. M. O'Connor
Keyword(s):  
High Resolution ◽  
Spatial Scales ◽  
Systematic Errors ◽  
Ozone Production ◽  
Systematic Effect ◽  
Model Errors ◽  
Low Resolution ◽  
Production Rates ◽  
Transport Models ◽  
The Difference

Abstract. Limited resolution in chemistry transport models (CTMs) is necessarily associated with systematic errors in the calculated chemistry, due to the artificial mixing of species on the scale of the model grid (grid-averaging). Here, the errors in calculated hydroxyl radical (OH) concentrations and ozone production rates 3 are investigated quantitatively using both direct observations and model results. Photochemical steady-state models of radical chemistry are exploited in each case to examine the effect on both OH and 3 of averaging relatively long-lived precursor species, such as O3, NOx, CO, H2O, etc. over different spatial scales. Changes in modelled 3 are estimated, independently of other model errors, by calculating the systematic effect of spatial averaging on the ozone production efficiency 1, defined as the ratio of ozone molecules produced per NOx molecule destroyed. Firstly, an investigation of in-flight measurements suggests that, at least in the northern midlatitude upper-troposphere/lower stratosphere, averaging precursor species on the scale of a T42 grid (2.75° x 2.75°) leads to a 15-20% increase in OH concentrations and a 5-10% increase in 1. Secondly, results from CTM model experiments are compared at different horizontal resolutions. Low resolution experiments are found to have significantly higher [OH] and 3 compared with high resolution experiments. The extent to which these differences may be explained by the systematic error in the model chemistry associated with grid size is estimated by degrading the high resolution data onto a low resolution grid and then recalculating 1 and [OH]. The change in calculated 1 is found to be significant and can account for much of the difference in 3 between the high and low resolution experiments. The calculated change in [OH] is less than the difference in [OH] found between the experiments, although the shortfall is likely to be due to the indirect effect of the change in modelled NOx, which is not accounted for in the calculation. It is argued that systematic errors caused by limited resolution need to be considered when evaluating the relative impacts of different pollutant sources on tropospheric ozone.

scholarly journals Systematic Error Correction of Dynamical Seasonal Prediction of Sea Surface Temperature Using a Stepwise Pattern Project Method

2008 ◽  
Vol 136 (9) ◽  
pp. 3501-3512 ◽  
Author(s):  
Jong-Seong Kug ◽  
June-Yi Lee ◽  
In-Sik Kang
Keyword(s):  
Statistical Model ◽  
Systematic Error ◽  
Error Correction ◽  
Statistical Error ◽  
Temporal Correlation ◽  
Systematic Errors ◽  
Climate Prediction ◽  
Global Ocean ◽  
Coupled Gcm ◽  
Mean State

Abstract Every dynamical climate prediction model has significant errors in its mean state and anomaly field, thus degrading its performance in climate prediction. In addition to correcting the model’s systematic errors in the mean state, it is also possible to correct systematic errors in the predicted anomalies by means of dynamical or statistical postprocessing. In this study, a new statistical model has been developed based on the pattern projection method in order to empirically correct the dynamical seasonal climate prediction. The strength of the present model lies in the objective and automatic selection of optimal predictor grid points. The statistical model was applied to systematic error correction of SST anomalies predicted by Seoul National University’s (SNU) coupled GCM and evaluated in terms of temporal correlation skill and standardized root-mean-square error. It turns out that the statistical error correction improves the SST prediction over most regions of the global ocean with most forecast lead times up to 6 months. In particular, the SST predictions over the western Pacific and Indian Ocean are improved significantly, where the SNU coupled GCM shows a large error.

scholarly journals The Potential for Self-Organizing Maps to Identify Model Error Structures

2014 ◽  
Vol 142 (4) ◽  
pp. 1688-1696 ◽  
Author(s):  
Walter C. Kolczynski ◽  
Joshua P. Hacker
Keyword(s):  
Direct Analysis ◽  
Systematic Errors ◽  
Use Of Self ◽  
Model Errors ◽  
Self Organizing Maps ◽  
Numerical Weather Model ◽  
Weather Model ◽  
Model Improvement ◽  
Spatially Varying ◽  
Self Organizing

Abstract An important aspect of numerical weather model improvement is the identification of deficient areas of the model, particularly deficiencies that are flow dependent or otherwise vary in time or space. Here the authors introduce the use of self-organizing maps (SOMs) and analysis increments from data assimilation to identify model deficiencies. Systematic increments reveal time- and space-dependent systematic errors, while SOMs provide a method for categorizing forecasts or increment patterns. The SOMs can be either used for direct analysis or used to produce composites of other fields. This study uses the forecasts and increments of 2-m temperature and dry column mass perturbation μ over a 4-week period to demonstrate the potential of this technique. Results demonstrate the potential of this technique for identifying spatially varying systematic model errors.

scholarly journals The effect of systematic error on the accuracy of Michaelis constants and maximum velocities estimated by using the integrated Michaelis–Menten equation (Short Communication)

1974 ◽  
Vol 143 (3) ◽  
pp. 779-781 ◽  
Author(s):  
Peter F. J. Newman ◽  
Gordon L. Atkins ◽  
Ian A. Nimmo
Keyword(s):  
Reaction Time ◽  
Systematic Error ◽  
Substrate Concentration ◽  
Unknown Parameter ◽  
Short Communication ◽  
Systematic Errors ◽  
Product Concentration ◽  
Initial Substrate ◽  
Michaelis Constants ◽  
Initial Substrate Concentration

Systematic errors in initial substrate concentration (s0), product concentration and reaction time give much larger errors in the Michaelis–Menten parameters unless s0 is treated as an unknown parameter. These errors are difficult to detect because the fitted curve deviates little from the data. The effect of non-enzymic reaction is also examined.

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