Wang Zhen, Jin Bingwen, Geng Weidong
College of Computer Science and Technology, Zhejiang University, Zhejiang 310000, China.
Sensors (Basel). 2017 Apr 8;17(4):806. doi: 10.3390/s17040806.
The poses of base station antennas play an important role in cellular network optimization. Existing methods of pose estimation are based on physical measurements performed either by tower climbers or using additional sensors attached to antennas. In this paper, we present a novel non-contact method of antenna pose measurement based on multi-view images of the antenna and inertial measurement unit (IMU) data captured by a mobile phone. Given a known 3D model of the antenna, we first estimate the antenna pose relative to the phone camera from the multi-view images and then employ the corresponding IMU data to transform the pose from the camera coordinate frame into the Earth coordinate frame. To enhance the resulting accuracy, we improve existing camera-IMU calibration models by introducing additional degrees of freedom between the IMU sensors and defining a new error metric based on both the downtilt and azimuth angles, instead of a unified rotational error metric, to refine the calibration. In comparison with existing camera-IMU calibration methods, our method achieves an improvement in azimuth accuracy of approximately 1.0 degree on average while maintaining the same level of downtilt accuracy. For the pose estimation in the camera coordinate frame, we propose an automatic method of initializing the optimization solver and generating bounding constraints on the resulting pose to achieve better accuracy. With this initialization, state-of-the-art visual pose estimation methods yield satisfactory results in more than 75% of cases when plugged into our pipeline, and our solution, which takes advantage of the constraints, achieves even lower estimation errors on the downtilt and azimuth angles, both on average (0.13 and 0.3 degrees lower, respectively) and in the worst case (0.15 and 7.3 degrees lower, respectively), according to an evaluation conducted on a dataset consisting of 65 groups of data. We show that both of our enhancements contribute to the performance improvement offered by the proposed estimation pipeline, which achieves downtilt and azimuth accuracies of respectively 0.47 and 5.6 degrees on average and 1.38 and 12.0 degrees in the worst case, thereby satisfying the accuracy requirements for network optimization in the telecommunication industry.
基站天线的姿态在蜂窝网络优化中起着重要作用。现有的姿态估计方法基于由塔上作业人员进行的物理测量或使用附着在天线上的额外传感器。在本文中，我们提出了一种基于天线的多视图图像和手机捕获的惯性测量单元（IMU）数据的新型非接触式天线姿态测量方法。给定天线的已知三维模型，我们首先从多视图图像中估计天线相对于手机摄像头的姿态，然后利用相应的IMU数据将姿态从摄像头坐标系转换到地球坐标系。为了提高结果的准确性，我们通过在IMU传感器之间引入额外的自由度并基于下倾角和方位角定义一个新的误差度量（而不是统一的旋转误差度量）来改进现有的摄像头-IMU校准模型，以完善校准。与现有的摄像头-IMU校准方法相比，我们的方法在保持下倾角精度相同水平的同时，平均方位精度提高了约1.0度。对于摄像头坐标系中的姿态估计，我们提出了一种自动初始化优化求解器并对结果姿态生成边界约束的方法，以实现更高的精度。通过这种初始化，当接入我们的流程时，最先进的视觉姿态估计方法在超过75%的情况下产生了令人满意的结果，并且我们利用这些约束的解决方案在平均下倾角和方位角上（分别低0.13度和0.3度）以及在最坏情况下（分别低0.15度和7.3度）实现了更低的估计误差，这是根据对由65组数据组成的数据集进行的评估得出的。我们表明，我们的两项改进都有助于所提出的估计流程的性能提升，该流程平均下倾角和方位角精度分别达到0.47度和5.6度，在最坏情况下分别为1.38度和12.0度，从而满足了电信行业网络优化的精度要求。
