# Visual-Based Localization Using Pictorial Planar Objects in Indoor Environment
## 6 Performance Study
### Pose Correctness  
To compare the VBL performance under different camera distances, environment brightness and noise conditions, we position the camera at different distances (100 cm, 150 cm, 200 cm and 300 cm) from the picture markers, and shoot video under normal, low brightness and noise conditions. We add gaussian noises to the images to simulate the noise condition. We use Three correctness criteria on evaluating the results. MAE refers to the mean absolute error (in pixels) between reported outline of the objects and their pose in ground truth. Diagonal is the sum of two diagonal distances of the pictorial object's corners. MAER, shows the matching error relative to the marker's size on the camera view, is the ratio of MAE and diagonal distances.  
The test results are shown in Table 2. The performances of three methods are all good in a normal brightness environment with the camera distance of 100-150 cm. Whereas PicPose performs better in a low brightness environment as both ArUco and ArPico only detect the corner point to calculate the pose. When the picture is in a dark environment, its corner and contour may not easily be differentiated from the background. Compared with those two methods, PicPose uses texture features in the whole picture which can produce more features to be detected in a dark environment. The MAE and MAER of all three methods become bigger with increasing distances. PicPose is better than ArUco and ArPico for the distance of 300 cm, especially under low brightness conditions. As for the images with noise, the MAEs of both ArUco and ArPico are lightly affected, meanwhile PicPose performed better than former as it has Bilateral filtering to reduce noises.  
The second comparison shows the reported distance between the camera and markers using ArUco, ArPico and PicPose for the same experimental setup. The result is shown in Table 3. When the camera distance is 100-200 cm, the results of ArUco and ArPico are slightly better than that of PicPose. However, PicPose performs better when the camera distance is 300 cm since it can get more matching points than ArUco and ArPico as the picture size gets smaller in the camera view.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Distance (cm)** & **Condition** & \multicolumn{2}{c}{**ArUco**} & \multicolumn{2}{c}{**ArPico**} & \multicolumn{2}{c}{**PicPose**} \\ \hline  & & **MAE/Diagonal** & **MAER** & **MAE/Diagonal** & **MAER** & **MAE/Diagonal** & **MAER** \\ \hline
100 & Normal & 4/966 & 0.4\% & 4/954 & 0.4\% & 5/975 & 0.4\% \\
100 & Low Brightness & 7/966 & 0.7\% & 7/954 & 0.7\% & 6/975 & 0.6\% \\
100 & With Noise & 10/966 & 1\% & 11/954 & 1.2\% & 8/975 & 0.8\% \\
150 & Normal & 6/651 & 0.9\% & 6/668 & 0.9\% & 5/657 & 0.8\% \\
150 & Low Brightness & 9/651 & 1.4\% & 8/668 & 1.3\% & 6/657 & 0.9\% \\
150 & With Noise & 16/651 & 2.5\% & 13/668 & 1.9\% & 10/657 & 1.5\% \\
200 & Normal & 7/486 & 1.4\% & 7/481 & 1.5\% & 7/491 & 1.4\% \\
200 & Low Brightness & 9/486 & 1.9\% & 10/481 & 2.0\% & 7/491 & 1.4\% \\
200 & With Noise & 16/486 & 3.3\% & 17/481 & 3.5\% & 14/491 & 2.8\% \\
300 & Normal & 5/326 & 1.5\% & 6/334 & 1.8\% & 4/325 & 1.2\% \\
300 & Low Brightness & 7/326 & 2.1\% & 8/334 & 2.4\% & 5/325 & 1.5\% \\
300 & With Noise & 18/326 & 5.5\% & 15/334 & 4.5\% & 10/325 & 3.0\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Pictorial Marker Matching Error.  
Figure 14: PicPose Robot Localization Accuracy.