# Visual-Based Localization Using Pictorial Planar Objects in Indoor Environment
## 7 Conclusions and Future Work  
In this paper, we have constructed a universal pictorial planner object based VBL framework. Based on this framework, we presented two centimeter-level localization methods using ArPico  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Distance (cm)** & **ArUco** & **ArPico** & **PicPose (10\%-View)** & **PicPose (30\%-View)** & **PicPose (50\%-View)** & **PicPose (90\%-View)** \\ \hline
100 & 0/335 & 0/234 & 56/302 (18.5\%) & 143/336 (42.6\%) & 264/298 (88.6\%) & 321/321 (100\%) \\
200 & 0/267 & 0/321 & 6/289 (2\%) & 114/321 (35.5\%) & 268/313 (85.6\%) & 303/307 (98.7\%) \\
300 & 0/352 & 0/333 & 0/325 (0\%) & 30/316 (9.5\%) & 156/290 (53.8\%) & 297/323 (92.5\%) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Partially Visible Picture Pose Success Ratio.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Truth Distance (cm)** & **Light Level** & **ArUco (cm)** & **ArPico (cm)** & **PicPose (cm)** \\ \hline
100 & Normal & 100.8 & 101.2 & 101.5 \\
100 & low & 101.6 & 101.1 & 101.3 \\
150 & Normal & 149.6 & 150.2 & 149.2 \\
150 & low & 148.1 & 149.5 & 148.7 \\
200 & Normal & 200.2 & 200.3 & 200.8 \\
200 & low & 201.9 & 201.1 & 201.7 \\
300 & Normal & 302.7 & 303.8 & 302.5 \\
300 & low & 306.4 & 306.2 & 305.9 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Reported Pose Distance.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Distance (cm)** & \multicolumn{4}{c}{**PicPose (ms)**} & \multicolumn{4}{c}{**ArUco (ms)**} & **ArPico (ms)** \\ \hline  & SSD & Extraction & Matching & Position & Total & \\ \hline
100 & 58.7 & 14.3 & 8.2 & 1.1 & 82.3 & 27.3 & 30.5 \\
200 & 64.1 & 12.7 & 7.9 & 1.0 & 85.7 & 29.4 & 31.2 \\
300 & 63.3 & 8.3 & 8.2 & 1.1 & 80.9 & 28.1 & 34.4 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Localization Execution Time.  
and PicPose, which detect the objects with pictorial surfaces in an indoor environment to provide device location. Using our proposed technology, mobile devices only need to equip with an on-board monocular camera to achieve indoor localization. As long as there is a 3D map consisting of known planar surfaces, the location can be generated precisely and efficiently. This is a low cost solution which makes the visual based localization method very scalable and flexible. Our proposed localization technology can be applied by any moving device with a camera, such as robot, smartphone, and smart glasses, as long as clear views on the environmental objects can be captured.  
Our VBL methods are built on top of existing image processing technology for picture identification and matching. We are enhancing many of them and also studying new machine learning models for picture recognition and matching. Our overall goal is to build an efficient VBL technology that can be deployed on light-weight indoor devices for future smart applications.  
Conceptualization, methodology, investigation, data curation, formal analysis, writing-original draft preparation, review and editing, Y.M. and K.-J.L.; software and validation, Y.M., B.-L.T., C.-C.C. and Y.C.; resources, Y.M., K.-J.L. and B.-L.T.; visualization, Y.M. and C.-C.C.; supervision and project administration, K.-J.L. and B.Z.; funding acquisition, Y.M., K.-J.L. and B.Z.. All authors have read and agreed to the published version of the manuscript.  
This research was supported in part by the Ministry of Science and Technology of Taiwan (MOST 108-2633-E-002-001), National Taiwan University (NTU-108L104039), Intel Corporation, Delta Electronics, Compal Electronics, National Natural Science Foundation of China (U1908212) and the Fundamental Research Funds for the Central Universities (N2017013, N2017014). [PERSON] was supported by the China Scholarship Council (201706080026).  
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.