Visual Localization for Copter based on 3D Model of Environment with CNN Segmentation

Buyval, Alexander; Mustafin, Ruslan; Gavrilenkov, Mikhail; Gabdullin, A. Sh.; Shimchik, Ilya

doi:10.22323/1.300.0035

Cited by 1 publication

(1 citation statement)

References 8 publications

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“…Zhou et al [19] developed a robot positioning method based on line detection, which extracts straight line features from images, applies transformations such as rotation and translation, determines the distance from the field of view center to the straight line, and achieves robot positioning. Alexander Buyval et al [20] introduced an indoor visual positioning method based on particle filter segmentation and the nearest edge. This approach detects image edges and maps them to a 3D model of the room for positioning, eliminating the influence of factors such as texture noise on visual positioning.…”

Section: Recent Advances In Image-based Localization Techniquesmentioning

confidence: 99%

Multi-granularity and Multi-modal Feature Fusion for Indoor Positioning

Pei,

Wang,

Zhao

et al. 2024

Preprint

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Despite the widespread use of indoor positioning technology, current WiFi positioning methods face challenges in accuracy and efficiency due to the complexities of indoor environments and the intricate architecture resulting from pre-deployed Access Points (APs). Moreover, while visual image positioning methods are effective, their costliness stemming from the requirement of using binocular cameras to obtain depth image data hinders their widespread adoption. To tackle these challenges, we propose an indoor positioning method that leverages multi-granularity and multi-modal feature fusion. In the initial, coarse-grained positioning phase, a neural network model is trained using image data to roughly localize indoor scenes. This model adeptly captures the geometric relationships within images, providing a foundation for more precise localization in subsequent stages. In the fine-grained positioning stage, Channel State Information (CSI) features from WiFi signals and Scale-Invariant Feature Transform (SIFT) features from image data are fused, creating a rich feature fusion fingerprint library that enables high-precision positioning. Experimental results show that our proposed method synergistically combines the strengths of WiFi fingerprint and visual positioning, resulting in a substantial enhancement of positioning accuracy. Specifically, our approach achieves an accuracy of 0.4 meters for 45\% of positioning points and 0.8 meters for 67\% of points. Overall, this approach charts a promising path forward for advancing indoor positioning technology.

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Section: Recent Advances In Image-based Localization Techniquesmentioning

confidence: 99%