Department of Information and Communication, School of Electronics and Information, Northwestern Polytechnical University, Xi'an 710129, China.
Sensors (Basel). 2018 Sep 17;18(9):3134. doi: 10.3390/s18093134.
Considering the radio-based indoor positioning system pertaining to signal degradation due to the environmental factors, and rising popularity of IP (Internet Protocol) cameras in cities, a novel fusion of inertial measurement units (IMUs) with external IP cameras to determine the positions of moving users in indoor environments is presented. This approach uses a fine-tuned Faster R-CNN (Region Convolutional Neural Network) to detect users in images captured by cameras, and acquires visual measurements including ranges and angles of users with respect to the cameras based on the proposed monocular vision relatively measuring (MVRM) method. The results are determined by integrating the positions predicted by each user's inertial measurement unit (IMU) and visual measurements using an EKF (Extended Kalman Filter). The results experimentally show that the ranging accuracy is affected by both the detected bounding box's by Faster R-CNN height errors and diverse measuring distances, however, the heading accuracy is solely interfered with bounding box's horizontal biases. The indoor obstacles including stationary obstacles and a pedestrian in our tests more significantly decrease the accuracy of ranging than that of heading, and the effect of a pedestrian on the heading errors is greater than stationary obstacles on that. We implemented a positioning test for a single user and an external camera in five indoor scenarios to evaluate the performance. The robust fused IMU/MVRM solution significantly decreases the positioning errors and shows better performance in dense multipath scenarios compared with the pure MVRM solution and ultra-wideband (UWB) solution.
考虑到基于无线电的室内定位系统会因环境因素导致信号衰减，以及城市中 IP（互联网协议）摄像机的普及，提出了一种将惯性测量单元 (IMU) 与外部 IP 摄像机融合的新方法，以确定室内环境中移动用户的位置。该方法使用经过微调的 Faster R-CNN（区域卷积神经网络）来检测摄像机拍摄的图像中的用户，并根据所提出的单目视觉相对测量 (MVRM) 方法，获取包括用户相对于摄像机的距离和角度在内的视觉测量值。通过将每个用户的惯性测量单元 (IMU) 预测的位置与使用 EKF（扩展卡尔曼滤波器）的视觉测量值进行集成，来确定结果。实验结果表明，测距精度受到 Faster R-CNN 检测到的边界框高度误差和不同测量距离的影响，而航向精度仅受边界框的水平偏差影响。在我们的测试中，室内障碍物（包括静止障碍物和行人）对测距精度的影响大于对航向精度的影响，而行人对航向误差的影响大于静止障碍物的影响。我们在五个室内场景中为单个用户和一个外部摄像机实施了定位测试，以评估性能。与纯 MVRM 解决方案和超宽带 (UWB) 解决方案相比，稳健的融合 IMU/MVRM 解决方案显著降低了定位误差，并且在密集多径场景中表现出更好的性能。
