High-precision indoor positioning method based on joint vision and wireless signal characteristics

Abstract

The invention discloses a high-precision indoor positioning method based on joint vision and wireless signal characteristics, and the method comprises the steps: in the offline stage, a to-be-positioned indoor site is collected, a WiFi fingerprint database and an image database are constructed, and the scene information of an environment is obtained; and in the online stage, a mobile terminal collects WiFi fingerprint data and image data in real time, performs coarse positioning on the collected WiFi fingerprint data, determines a potential area of a user, and then adopts a method based on deep neural network regression for the image data of the coarse positioning area to complete prediction of an accurate positioning position. According to the indoor positioning method, the wireless signal characteristics and the visual characteristics are fused so that the positioning error is further reduced while the calculation resources are reduced and the calculation complexity is reduced, and the high-precision indoor positioning is realized.

Description

technical field [0001] The present invention relates to a technology in the field of indoor wireless positioning, specifically a high-precision indoor positioning method based on joint vision and wireless signal characteristics, applicable to various indoor positioning systems equipped with Wi-Fi and cameras, such as the current number Numerous mobile robots and other smart devices. Background technique [0002] The existing positioning methods based on Wi-Fi location fingerprints need to accurately model the complex environment to mitigate the impact of multipath propagation, and the instantaneous signal fluctuations caused by some unpredictable obstacles (such as pedestrian movement) Very sensitive, causing attenuation and distortion of wireless signals. [0003] Some improved technologies combine visual information for real-time positioning in a targeted manner, but this type of technology needs to rely on cameras to collect images of the surrounding environment of the u...

AI Technical Summary

Problems solved by technology

However, due to the change of the internal parameters of the camera, the motion blur in the camera acquisition process, and the influence of external environmental factors such as changes in the lighting conditions in the indoor scene, the positioning accuracy is greatly limited, and it takes a lot of manpower and material resources to update the image database on time.

Moreover, most of these technologies perform signal-based positioning and vision-based positioning separately, only using the positioning results of the former to select some candidate areas to limit the processing complexity of the latter positioning stage, but ignoring the differences between the two schemes. Interaction

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