Positioning method and device based on environment image

Abstract

The invention discloses a positioning method and device based on an environment image. Area features of all areas contained in the obtained environment image serve as positioning bases. Regional features represented by each region are distinctly different from features of other regions in the environment image, so that the identifiability among the regions is improved, and the phenomenon of confusion among the regions is avoided. Furthermore, each region has region characteristics obviously different from those of other regions, so that each region can be used as a positioning basis to different extents, and even if some regions in the environment image are poor in quality (such as being shielded), positioning can be realized through other regions with good quality in the environment image. Through the method and the device in the specification, the phenomenon of positioning failure can be avoided to a greater extent, and the positioning success rate, the positioning accuracy and the positioning precision can be improved.

Description

technical field [0001] This specification relates to the field of unmanned driving technology, in particular to a positioning method and device based on environmental images. Background technique [0002] At present, as an important part of artificial intelligence technology, unmanned driving technology is playing an increasingly prominent role in social production and life, and has become one of the main directions to guide the development of transportation technology. [0003] With the development of unmanned driving technology, the methods for controlling the driving of intelligent vehicles (including unmanned vehicles and vehicles with assisted driving functions) are becoming more and more mature. Generally, during the running process of the smart vehicle, the smart vehicle needs to obtain its own location information (that is, determine the location information through the smart vehicle positioning method), so that the smart vehicle can travel according to the planned r...

AI Technical Summary

Problems solved by technology

[0004] However, in actual use, it is unavoidable that the feature points in the environment image are blocked by obstacles, and the feature points cannot be extracted, which leads to the inability to locate

In addition, when the collection range of the environmental image is large, the number of feature points that can be extracted is large, and it is difficult to ensure that there will be no confusion between the feature points, which will affect the positioning quality

[0005] It can be seen that the existing methods for locating intelligent vehicles through environmental images have many defects such as low positioning accuracy and low positioning success rate.

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