A satellite map-assisted navigation and positioning method based on deep learning

Abstract

The present invention is a satellite map-assisted navigation and positioning method based on deep learning. The method is applied to an unmanned aerial vehicle to solve the problem of navigation and positioning when GNSS loses lock. Including: step 1, according to the drift error of the inertial navigation device, selecting image landmark points in the flight area. Step 2: Establish a target detection dataset based on satellite remote sensing images at landmark points, and use the improved YOLO v3 for training. Step 3: During the flight, according to the trained model and model parameters, the landmark points with known position information are detected, and the position of the UAV is obtained through the coordinate transformation relationship. Step 4: Kalman filter is used to fuse it with the position parameters output by the INS system to realize integrated navigation. Aiming at the situation that the GNSS of the unmanned aerial vehicle loses lock, the invention realizes auxiliary navigation and positioning by using target detection, and effectively solves the problem of cumulative drift error of a single INS system.

Description

technical field [0001] The invention relates to the field of unmanned aerial vehicles, in particular to the field of satellite map-assisted navigation and positioning based on deep learning, and belongs to the technical fields of unmanned aerial vehicles, autonomous navigation, and target detection. Background technique [0002] During the flight of UAVs, if the inertial navigation equipment is not corrected, it will form cumulative drift errors. One of the common solutions is INS / GNSS integrated navigation, but in complex electromagnetic environments, GNSS signals are susceptible to interference. With the development of computer vision, and visual sensors are hardly affected by the electromagnetic environment, visual navigation technology has been widely researched and applied in recent years. When the environment is unknown and the task area is small, SLAM (simultaneous localization and mapping) can be used for map construction and positioning. Aiming at the positioning p...

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Problems solved by technology

Aiming at the positioning problem of UAVs in complex electromagnetic environments, XiJia Liu et al. proposed interval storage of known maps on routes, and used improved SIFT to extract the features of these images. During the flight, the real-time aerial images were combined with the features The real-time position of the UAV is obtained after the coordinate solution. The disadvantage of this method is that the feature extraction and matching effect of the remote sensing image is not good, and storing a large amount of feature information is very difficult for the memory of the UAV onboard computer. big consumption

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