Abstract
Abstract. Indoor positioning has attracted much attention in recent years due to the trend of Internet of Things (IoT), which is capable of providing numerous applications such as personal tracking, vehicle locator, and Location-Based Service (LBS). To put LBS into practice, positioning and navigation are one of the necessary techniques. Using the smartphone to process indoor positioning also become more and more usual. The most common algorithm in the inertial navigation system is Pedestrian Dead Reckoning (PDR), utilizing sensors built-in the smartphones to conquer the strait of GNSS-denied environment. However, for the purpose of eliminating the error accumulated with time, PDR combining with other algorithms, for instance, updating some geospatial information steadily is a better way to solve this problem. Therefore, this research proposes the imaged based aided algorithm. Moreover, in this study, a novel Artificial Neural Networks (ANN) embedded the system is proposed. The self-designed georeferenced markers and the indoor floor plan will be produced by an Indoor Mobile Mapping System (IMMS) in advance. This research proposed using Cascade-Correlation neural Network (CCN) to estimate the distance between the marker and the smartphone camera. The accuracy using this method can achieve to 0.27 meter. As if at least three coordinates and the distance can be obtained simultaneously, the position of the user can be calculated by the trilateration method. From the experiment, the accuracy of the positioning is about 0.5 meter. This way seems to have the high potential to bring into play on the real-time indoor positioning.
Highlights
As the development of Global Navigation Satellite System (GNSS) from the 1970s, the performance of outdoor positioning and navigation is outstanding
After acquiring the coordinate of the marker, this study proposes utilizing Cascade-Correlation neural Network (CCN) to estimate the distance between the marker and the camera
This study proposes using the deformational parameters as the input of CCN to evaluate the distance
Summary
As the development of Global Navigation Satellite System (GNSS) from the 1970s, the performance of outdoor positioning and navigation is outstanding. For Bluetooth beacons based, it's capable of achieving up to 1-meter level accuracy. This study proposes using the image-based technique to do the localization, whose advantages include no additional devices and only need an off-the-shelf smartphone with a built-in camera. The algorithms of using image-based technology can be separated into two categories, feature points tracking and marker recognition (Davison, 2003). The accuracy of the former may be affected by the lighting condition. In order to avoid those challenges, this study is applied to using the self-designed marker to do positioning This method can speed up the processing time by detecting certain points. The accuracy of the result can achieve 0.5 meter, which is available for indoor positioning
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