# Calibration Method of Orthogonally Splitting Imaging Pose Sensor Based on General Imaging Model
## 4 Discussion  
The orthogonally splitting imaging pose sensor owns an autonomously designed optical imaging structure with assembly errors and various nonlinear and linear distortions. On the contrary, the pinhole imaging model cannot accurately describe the special optical imaging system. This paper proposes a calibration method based on general imaging models. The general imaging model introduces the Plucker coordinate to represent the mapping relationship between the world coordinate and the image coordinate and distortion does not need to be considered. It only cares about the mapping relationship between the incoming light and the pixel, and is suitable for the modeling of various optical systems. The method uses radial basis function interpolation to fit the mapping relationship between target points in the world coordinate system and image points in the image coordinate system. The calibration data requires that each target point in the world coordinate system only needs one corresponding image in the image coordinate system. Because the distribution of control points will affect the accuracy of radial basis function interpolation, this paper adopts Kmeans clustering method to adaptively select the control points set, which effectively improves the fitting accuracy. This paper uses the calibration method based on the general imaging model for the calibration of the orthogonally splitting imaging pose sensor. Through experiments, the appropriate radial basis function and its shape parameters are selected. The experimental results show that the MQ function  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Calibration Dataset RMS (mm)** & **Test Dataset RMS (mm)** \\ \hline MQ function & 0.048 & 0.049 \\ Gaussian function & 0.0778 & 0.0784 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Error valuation of calibration dataset and test dataset.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **General Imaging Model** & **Pinhole Imaging Model** \\ \hline RMS (mm) & 0.048 & 0.061 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Error valuation of general imaging model and pinhole imaging model.  
is more suitable for the orthogonally splitting imaging pose sensor as a radial basis function, RMS of calibration data set and RMS of test data set are 0.048 mm and 0.049 mm. Compared with the calibration method based on the pinhole imaging model, the calibration accuracy of the calibration method based on the general imaging model is higher. Therefore, the calibration method based on the general imaging model proposed in this paper is suitable for the calibration of the orthogonally splitting pose sensor. In the future, the calibration method based on the general imaging model can be applied to more types of optical imaging systems, and correspondingly interpolation methods should be explored to improve calibration accuracy. The application of the calibration method can be verified through more calibration experiments and test experiments.  
Conceptualization, C.S.; Writing-original draft, N.Z.; Writing-review & editing, P.W.  
This research was funded by the National Natural Science Foundation of China grant number [No.51375339].  
We thank [PERSON] and [PERSON] for contribution in the preliminary work of the experiment.  
The authors declare no conflicts of interest.