類神經網路輔助影像室內定位

Mei Chin Hung, Jhen Kai Liao, Yu Hua Li, Kai Wei Chiang, Jing Shiou Wang, Jiu Fu Huang, Jiun Yi Wu

研究成果: Article

摘要

With the springing up of smartphones, indoor navigation becomes more and more popular. One of the algorithms in the domain of indoor navigation is Pedestrian Dead Reckoning (PDR), which has the good potential to confront the challenge of the blocked satellite signal. Moreover, the error of inertial sensors accumulating with time can be solved by updating geospatial information steadily. This study adopts a method based on the built-in sensors combining with the camera. In order to reduce the image processing, the study further adopts the marker self-designed to aid in carrying out indoor positioning. Then, the Artificial Neural Network (ANN) is applied to estimate the distance between the marker and the camera. Because the marker is also georeferenced, the position of camera is calculated through the detected georeferenced marker, estimated distance. Afterward, the result of PDR can be updated. In this study, the result shows that the accuracy using Multi-Layer Feed-Forward Neural Networks (MFNNs) is higher than traditional techniques. However, the architecture still can't overcome the catastrophic forgetting in the neural network. For this predicament, this study proposes using Cascade Correlation Networks (CCNs) and adding the key data to improve accuracy. As a result, based on the same training data, trying to add some key data makes the accuracy can achieves 0.5 meters.

貢獻的翻譯標題Indoor Positioning Based-on Images Aided by Artificial Neural Networks
原文???core.languages.zh_ZH???
頁(從 - 到)529-533
頁數5
期刊Journal of the Chinese Institute of Civil and Hydraulic Engineering
31
發行號6
DOIs
出版狀態Published - 2019 十月 1

All Science Journal Classification (ASJC) codes

  • Civil and Structural Engineering

指紋 深入研究「類神經網路輔助影像室內定位」主題。共同形成了獨特的指紋。

引用此