Visual indoor positioning method and system based on architectural planar graph prior information

Abstract

In order to solve the defects in the prior art, the invention provides a visual indoor positioning method and system based on architectural plane graph prior information, and the method comprises the steps: architectural plane graph preprocessing, data downsampling, input tensor construction, feature extraction and feature matching, registration and pose calculation, and model training. The method provided by the invention relates to an end-to-end deep learning model, the model innovatively introduces a K nearest neighbor distance histogram, a point cloud feature extraction network, a graph attention network, an optimal transmission algorithm and a self-supervised training method, so that the model has relatively strong robustness and adaptability, and the model has relatively small parameters and is easy to train and deploy; according to the method provided by the invention, the position of the equipment in the building body can be estimated only according to the building plane graph under the condition that the building body is not completely explored.

Description

technical field [0001] The method of the invention relates to the technical field of indoor positioning, and more particularly relates to a visual indoor positioning method and system based on prior information of a building plan. Background technique [0002] The indoor positioning problem has always been the basic problem of many real-world applications, such as AR house viewing, indoor navigation, on-site furniture layout design, building information system, mobile robots and other applications. At present, there are two main technical solutions for indoor positioning. One is the technology based on external beacons, such as ultra-wideband wireless communication technology positioning (UWB), Bluetooth, positioning QR code, etc., but such technologies rely on pre-positioning scenarios. Layout related beacons, and the other type is based on Simultaneous Localization and Mapping (SLAM) technology to achieve indoor positioning, which can use sensors such as lidar and cameras ...

AI Technical Summary

Problems solved by technology

However, in traditional visual SLAM technology, localization and mapping are two mutually coupled and simultaneous processes-localization depends on the landmark information provided by the mapping process, and the establishment of a map needs to determine its current position before it can be determined The observed landmark position, and the map coordinate system is only related to the initial position. If the device needs to know its specific position in the building environment at the beginning of positioning, then it is necessary to introduce a priori building structure information

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