An advanced multiple outlier detection algorithm for 3D similarity datum transformation

Ma, Youqing; Liu, Shaochuang; Li, Qunzhi

doi:10.1016/j.measurement.2020.107945

Cited by 16 publications

(5 citation statements)

References 47 publications

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“…In addition, the estimated Navcam pose parameters based on the proposed algorithm will be affected, or even severely distorted, when the tie points are contaminated by gross errors or outliers. Hence, future studies of the proposed algorithm are also needed to detect outliers under certain weighting conditions [29] and the total least squares solution [30].…”

Section: Discussionmentioning

confidence: 99%

A novel precise pose prediction algorithm for setting the sleeping mode of the Yutu-2 rover based on a multiview block bundle adjustment

Peng

et al. 2022

Robotica

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To set the sleeping mode for the Yutu-2 rover, a visual pose prediction algorithm including terrain reconstruction and pose estimation was first studied. The terrain reconstruction precision is affected by using only the stereo navigation camera (Navcam) images and the rotation angles of the mast. However, the hazard camera (Hazcam) pose is fixed, and an image network was constructed by linking all of the Navcam and Hazcam stereoimages. Then, the Navcam pose was refined based on a multiview block bundle adjustment. The experimental results show that the mean absolute errors of the check points in the proposed algorithm were 10.4 mm over the range of $\boldsymbol{L}$ from 2.0 to 6.1 m, and the proposed algorithm achieved good prediction results for the rover pose (the average differences of the values of the pitch angle and the roll angle were −0.19 degrees and 0.29 degrees, respectively). Under the support of the proposed algorithm, engineers have completed the remote setting of the sleeping mode for Yutu-2 successfully in the Chang’e-4 mission operations.

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Section: Discussionmentioning

confidence: 99%

A novel precise pose prediction algorithm for setting the sleeping mode of the Yutu-2 rover based on a multiview block bundle adjustment

Peng

et al. 2022

Robotica

Self Cite

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“…Second, to analyse the agreement between PhotoMeDAS 3D measurements and the ground truth, a standard similarity transformation [21], that is, a set of three shifts (T x , T y , T z ), rotations along the three axes (R x , R y , R z ), and a scale factor (1 + dS), can be determined. The seven parameters determined after the similarity transformation should not be significantly different from zero to ensure that both models are statistically compatible.…”

Section: Model Comparisonmentioning

confidence: 99%

Assessment of Cranial Deformation Indices by Automatic Smartphone-Based Photogrammetric Modelling

2022

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This paper presents research carried out to assess the accuracy of a fully automatic smartphone-based photogrammetric solution (PhotoMeDAS) to obtain a cranial diagnostic based on the 3D head model. The rigorous propagation of the coordinate measurement uncertainty to the infant’s derived cranial deformation indices is demonstrated. The cranial anthropometric parameters and cranial deformation indices that PhotoMeDAS calculates automatically were analysed based on the estimated accuracy and uncertainty. To obtain both accuracy and uncertainty, a dummy head was measured 54 times under different conditions. The same head was measured with a top-of-the-line coordinate-measuring machine (CMM), and the results were used as ground-truth data. It is demonstrated that the PhotoMeDAS 3D models are an average of 1.01 times bigger than the corresponding ground truth, and the uncertainties are around 1 mm. Even assuming uncertainties in the coordinates of up to 1.5 mm, the error in the derived deformation index uncertainties is around 1%. In conclusion, the PhotoMeDAS solution improves the uncertainty obtained in an ordinary paediatric consultation and can be recommended as a tool for doctors to establish an adequate medical diagnosis based on comprehensive cranial deformation indices, which is much more precise and complete than the information obtained by existing analogue devices (measuring tapes and callipers) and easier to use and less expensive than radiological imaging (CT and MRI).

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“…There are two schemes for handling gross errors. One is to classify it into a gross error detection of the function model [14][15][16], and the other is to classify it into a robust estimation of the stochastic model [17,18]. When the common point is deformed in only one coordinate component direction, the gross error detection method eliminates the point entirely, which results in a waste of data.…”

Section: Introductionmentioning

confidence: 99%

Robust solution of coordinate transformation parameters with a high breakdown point

Liu

et al. 2023

Meas. Sci. Technol.

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Because the M estimation method may lead to poor robustness or even failure owing to excessive outliers, a robust algorithm with a high breakdown point was proposed and applied to the estimation of coordinate transformation parameters. Firstly, the sampling method was used to calculate multiple sets of model parameters, and some sampling results were sifted according to posterior information. Then, the samples were sorted according to their number in the sampling results, and the F-test was adopted to screen and reserve valid information. Finally, the initial values of the reliable parameters were computed using the valid information, and the final parameters were obtained by the Institute of Geodesy and Geophysics (IGG) III scheme. Monte Carlo method was adopted for the simulation test, and a case analysis was chosen for verification. The results show that the proposed method can identify and process outliers more accurately than those of Rousseeuw [22] and Tao et al. [25]. When the proportion of outliers exceeded 50%, the proposed algorithm maintained a strong robustness and had a high breakdown point.

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An advanced multiple outlier detection algorithm for 3D similarity datum transformation

Cited by 16 publications

References 47 publications

A novel precise pose prediction algorithm for setting the sleeping mode of the Yutu-2 rover based on a multiview block bundle adjustment

A novel precise pose prediction algorithm for setting the sleeping mode of the Yutu-2 rover based on a multiview block bundle adjustment

Assessment of Cranial Deformation Indices by Automatic Smartphone-Based Photogrammetric Modelling

Robust solution of coordinate transformation parameters with a high breakdown point

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