An intelligent vehicle positioning method and system for a cognitive map

Abstract

The invention discloses an intelligent vehicle positioning method and system for a cognitive map. The method comprises the following steps: treating, cognitive image data are collected and preprocessed; SSD landmark detection, continuously carrying out traffic sign image landmark detection on the preprocessed graphic data by utilizing an SSD detection algorithm; ORB feature extraction, feature tracking and feature screening, road sign positioning, intelligent vehicle self-positioning and other processes are carried out, an intelligent vehicle positioning mode based on a cognitive map is adopted, the cognitive map of an intelligent vehicle is mainly constructed based on vision, and a system that the intelligent vehicle can complete vehicle positioning only through a camera is achieved; According to the invention, the use of a laser radar is avoided, the hardware cost of the intelligent vehicle is reduced, the real-time processing data of the system is reduced, the calculation use cost is reduced, the intelligent vehicle positioning can be realized with small map data volume and small calculation amount, and the problems of large high-precision map data volume, high use cost and thelike in the unmanned field are solved.

Description

technical field [0001] The invention relates to the field of driverless map positioning, in particular to a cognitive map-oriented intelligent vehicle positioning method and system. Background technique [0002] The positioning of traditional intelligent driving vehicles relies on satellite positioning, laser radar, and image matching. The positioning accuracy of ordinary GPS equipment is 2-5m, and the hardware investment cost of high-precision GPS equipment is high. Navigation systems based on satellite positioning are signal dependent. Firstly, the system positioning cannot be completed in the road section where the signal is blocked. Secondly, LiDAR positioning can achieve high-precision positioning through point cloud registration, but its disadvantages such as high cost, large amount of data, and large amount of calculation make LiDAR difficult It is suitable for the development of the next generation of smart cars. Image matching and positioning depend on the image dat...

AI Technical Summary

Problems solved by technology

[0002] The positioning of traditional intelligent driving vehicles relies on satellite positioning, laser radar, and image matching. The positioning accuracy of ordinary GPS equipment is 2-5m, and the hardware investment cost of high-precision GPS equipment is high. Navigation systems based on satellite positioning are signal dependent. Firstly, the system positioning cannot be completed in the road section where the signal is blocked. Secondly, LiDAR positioning can achieve high-precision positioning through point cloud registration, but its disadvantages such as high cost, large amount of data, and large amount of calculation make LiDAR difficult It is suitable for the development of the next generation of smart cars. Image matching and positioning depend on the image database. The storage of the image database requires a huge storage space. The search and matching process of the image in the database requires a huge amount of calculation, which is not suitable for large-scale mapping and positioning.

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