2020
DOI: 10.3390/ijgi9020074
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Integration of Convolutional Neural Network and Error Correction for Indoor Positioning

Abstract: With the rapid development of surveying and spatial information technologies, more and more attention has been given to positioning. In outdoor environments, people can easily obtain positioning services through global navigation satellite systems (GNSS). In indoor environments, the GNSS signal is often lost, while other positioning problems, such as dead reckoning and wireless signals, will face accumulated errors and signal interference. Therefore, this research uses images to realize a positioning service. … Show more

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Cited by 6 publications
(3 citation statements)
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“…Each layer contains several neurons, and information transfer between neurons is realized through weights and activation functions [ 44 ]. The neural network has obvious advantages in solving complex regression prediction problems with discretized data and has good performance in localization error correction [ 45 ].…”
Section: Methodsmentioning
confidence: 99%
“…Each layer contains several neurons, and information transfer between neurons is realized through weights and activation functions [ 44 ]. The neural network has obvious advantages in solving complex regression prediction problems with discretized data and has good performance in localization error correction [ 45 ].…”
Section: Methodsmentioning
confidence: 99%
“…One difficulty considered in computer vision is how to estimate people's locations in an interior space as precisely as workable. Changing the weight-of-loss function in a 23-layer CNN architecture [20]. Resize the pictures before the training step to keep the entire image as the CNN input value [21] suggested using a CNN model to generate important features and estimate camera settings for 3D reconstruction.…”
Section: Related Workmentioning
confidence: 99%
“…With the rapid development of CNN, more and more feature extraction networks have shown strong feature extraction ability. Many of them can be applied to existing computer vision tasks such as object detection [41], land-cover classification [42][43][44], and image matching [45][46][47]. For change detection tasks that may be regarded as pixel-level classification problems, a fully convolutional layer rather than a fully connected layer could achieve this [48].…”
Section: Feature Extractormentioning
confidence: 99%