Indoor mapping and positioning method based on target identification

Abstract

The invention relates to an indoor mapping and positioning method based on target identification. The method comprises steps that 1), a color depth image is inputted, and a target object model database is established based on the color depth image; 2), the color depth image measured in real time is inputted, the color depth image is converted into 3D point cloud, and a scene surface which can be seen from the present visual angle is further solved; 3), a camera attitude is estimated according to the 3D point cloud and based on the closest iteration point; 4), according to the 3D point cloud and the established target object model database, object identification is carried out through utilizing point pair characteristics, and an object attitude is estimated; 5), an attitude map is optimized through utilizing the camera attitude and the object attitude; and 6), the optimal object attitude is acquired according to the attitude map, surface rendering is carried out through utilizing the existing target object model database, and the three-dimensional map in the present environment is acquired. The method is advantaged in that indoor positioning and mapping are carried out under the plenty-of-object scene, mapping precision is high, and a few of calculation resources are utilized.

Description

technical field [0001] The invention belongs to the technical field of image processing and pattern recognition, and specifically relates to an indoor mapping and positioning method based on target recognition; indoor mapping and positioning are performed through target recognition assistance and optimization, and indoor maps and positioning with complete details can be obtained relatively quickly. Accurate camera pose localization. Background technique [0002] Image processing and pattern recognition is an interdisciplinary subject. In recent decades, image processing and pattern recognition technology has made remarkable progress, and has begun to move from the laboratory to the market. With the continuous development of image processing and pattern recognition research work, the recognition accuracy of recognition systems for different types of images and different patterns is already high enough to meet the needs of market users. It has been used in industries, home app...

AI Technical Summary

Problems solved by technology

The sparse map has a small amount of calculation and a fast calculation speed, but the problem is that the amount of information is small, which is not enough for the robot to complete the task

Dense maps have a large amount of information, but the amount of calculation is also large

[0004] At present, the dense method of indoor positioning and mapping uses images as the core. The limitation of this type of system is that it cannot understand the meaning of the map, and the process takes a lot of time

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