# Evaluation of RGB-D Multi-Camera Pose Estimation for 3D Reconstruction
## 5 Conclusions  
In this study, we evaluated three pose estimation methods for RGB-D (3D) cameras in the context of 3D point cloud reconstruction. We proposed a method to identify the point pairs, and performed error measurements. Using a charuco cuboid, we identified and labeled the corner of the ArUco markers in order to create a pairwise set of points to measure the error values.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **MAE** & **MSE** & **RMSE** \\ \hline Charuco cuboid & 1.2708 & 1.0987 \(\times\) 10\({}^{-2}\) & 2.9837 \\ Robotic Arm & 0.6983 & 3.1597 \(\times\) 10\({}^{-3}\) & 1.7432 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary results for cuboid and TCP reconstruction with MAE, MSE, and RMSE for filtered data.  
Two of our developed methods used the charuco board to estimate the pose of the cameras, whereas the third method used the robots' TCP position to calculate and transform the point clouds to reconstruct the scene. The data were captured with a fixed position and at a distance of around 1 m from the board in each direction.  
This distance reduces the resolution of the charuco board and consequently reduces the camera's ability to recognise markers on it. To improve the recognition of markers, a sharpening filter was applied. Further filters were used to mitigate artefacts in the point cloud, due to the stereo vision system used by the camera.  
Two methods, the cuboid and TCP methods, provided a good reconstruction with low RMSE values, taking into account the often noisy nature of point clouds derived from RGB-D cameras. The double-sided angled board had the highest RMSE, and it was excluded from further assessments. The increased error was assumed to be a result of the combination of the angle and distance from the camera. The robot's TCP position had a high accuracy, exhibiting the best overall performance.  
Conceptualisation, L.d.M.E., M.M., O.S. and A.P.; methodology, L.d.M.E.; software, I.d.M.E.; validation, A.M.; formal analysis, I.d.M.E.; investigation, L.d.M.E.; writing--original draft preparation, I.d.M.E.; writing--review and editing, A.M.; visualisation, L.d.M.E.; supervision, A.M. and P.J.F.; project administration, A.M.; funding acquisition, A.M. All authors have read and agreed to the published version of the manuscript.  
This work was partially supported by the Research Council of Norway through the funding to the \"MeaTable--Robotised cells to obtain efficient meat production for the Norwegian meat industry\" project no. 281234. The work is also, in part, supported by the EC H2020 project \"RoBUTCHER\" grant agreement no. 871631.  
The source code can be downloaded from [[https://github.com/imesper/Calibration.git](https://github.com/imesper/Calibration.git)]([https://github.com/imesper/Calibration.git](https://github.com/imesper/Calibration.git)) (accessed on 10 March 2022). For the data please contact the author at [EMAIL_ADDRESS].  
We wish to acknowledge the help provided by [PERSON] and [PERSON] from Byte Motion, and [PERSON] from RobotNorge for providing us with very valuable knowledge on the matter.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} TCP & Tool centre point \\ RMSE & Root mean squared error \\ MAE & Mean absolute error \\ SME & Squared mean error \\ CHU & Carcass handling unit \\ \end{tabular}