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題名 | 類神經網路輔助影像室內定位=Indoor Positioning Based-on Images Aided by Artificial Neural Networks |
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作 者 | 洪渼芹; 廖振凱; 李育華; 江凱偉; 王靚琇; 黃鉅富; 吳俊毅; | 書刊名 | 中國土木水利工程學刊 |
卷期 | 31:6 2019.10[民108.10] |
頁次 | 頁529-533 |
專輯 | 「智慧測量與工程大數據」特刊 |
分類號 | 440.98 |
關鍵詞 | 類神經網路; 多層前饋式神經網路; 階層關聯式神經網路; ANN; MFNN; CCN; |
語文 | 中文(Chinese) |
中文摘要 | 利用智慧型手機進行室內導航日趨流行,而行人航位推算法可透過感測器解決室內接收不到GNSS的狀況,其隨時間累積的誤差亦可透過穩定更新圖資改善。本研究導航系統利用具絕對坐標的圖資,影像辨識後再以類神經網路推算標誌與手機間距離。以多層前饋式神經網路架構輔助進行影像定位,成果顯示精度雖優於僅使用傳統影像定位技術,仍會出現學習新資訊忘記既有資訊的問題。本研究提出基於階層關聯式神經網路,同時採用過去訓練之關鍵數據,能有效改善上述遺忘過去訓練成果的問題,距離精度可提升至0.5公尺,可望未來實質運用於手機行人定位。 |
英文摘要 | 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. |
本系統之摘要資訊系依該期刊論文摘要之資訊為主。