# Estimation of Antenna Pose in the Earth Frame Using Camera and IMU Data from Mobile Phones
## 7 Discussion and Conclusions  
The focus of our study is the development of a novel non-contact solution for estimating antenna tilt and azimuth angles using a mobile phone as the measuring device. The two key points of our pipeline are the newly proposed camera-IMU calibration method for mobile phones and the coarse-to-fine visual pose estimation method.  
The major difference between our camera-IMU calibration method and the state-of-the-art [37; 50; 51] is the inclusion of additional DoFs between the accelerometer frame and the magnetometer frame, which allows for decoupling of the accelerometer-related error and the magnetometer-related error and therefore leads to good performance on both tilt and azimuth estimation tasks simultaneously.  
The crucial distinction between our visual pose estimation method and existing ones is the coarse-to-fine strategy we adopt. With this strategy, we avoid any manual pose initialization and more importantly are able to refine the approximate pose as a constrained optimization problem for higher accuracy compared with the state-of-the-art [32; 35]. Besides, our method is based on multi-view contours instead of stable visual feature, which makes it very suitable for pose estimation of the textureless and simple-shaped antennas.  
The major limitation of our work is the excessive computational resource consumption of the global optimization step of the pose refinement procedure. In the future, we will attempt to alleviate this problem by adding simple user interactions and/or developing more heuristic strategies for search space reduction.  
We are also aware of the influence of hand shakes on accelerometer outputs if no tripod is used. According to our experience, a simple mean filter applied on the accelerometer data can effectively reduce the impact provided the shakes are slight; nevertheless, we intend to exploit methods used in image stabilization to fundamentally address the issue. Another related problem is the simple strategy for fusion of pose measurements from multiple viewpoints: though the present method of averaging works well in most cases, it may fail to generate the optimal results when outliers exist, as indicated by the relatively large error in the last row of Table 4. To overcome this problem, we have two working directions in the future: one is to adopt more powerful fusion methods, and the other is to integrate information from more sensors for an effective quality metric for pose measurements.  
At last, we note that, aside from mobile telecommunications, our method can also be useful in areas such as the space field [64], indoor navigation [65], unmanned aerial vehicles [66], and so on.  
This work was supported by a grant from the National Natural Science Foundation of China (No. 61379067) and the National Key R&D Program of China (No. 2016 YFB1001300). [PERSON] and [PERSON] conceived of the idea. [PERSON] and [PERSON] performed the experiments and analyzed the data. [PERSON] wrote the paper. [PERSON] and [PERSON] assisted in revising and proofreading the paper.  
The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses or interpretation of data; in the writing of the manuscript; nor in the decision to publish the results.  
Figure 17: Two examples showing scenes with working antennas and the pose estimation results of our method.