Fish-eye camera stereo vision depth measuring method based on Taylor series model

Abstract

The invention discloses a fish-eye camera stereo vision depth measuring method based on a Taylor series model. Firstly, a fish-eye stereo vision camera is standardized according to the Taylor series model to obtain a mapping relation from a three-dimensional space point to a two-dimensional image point and a position relation between a left fish-eye stereo camera and a right fish-eye stereo camera, namely, the in parameter and the out parameter of the fish-eye stereo vision camera. On that basis, spherical polar line is corrected, so that the same three-dimensional space point is located in the same line on the corrected left image and right image. Finally, a matching relation of corresponding points is obtained on the left image and the right image by stereo matching, and depth information of corresponding three-dimensional space point is calculated. The fish-eye camera stereo vision depth measuring method based on the Taylor series model has the following advantages: a modeling method based on the Taylor series can achieve higher modeling precision for the fish-eye camera, and based on a spherical polar line correction method, computational complexity can be effectively reduced on the condition of guaranteeing correction precision.

Description

technical field [0001] The invention relates to a computer vision method for depth measurement using a fisheye camera, in particular to a fisheye camera stereo vision depth measurement method based on a Taylor series model. Background technique [0002] Compared with the field of view of ordinary cameras, the ultra-wide-angle fisheye camera can obtain a wide range of scene information, and the stereo vision system composed of it can measure the depth of a wide range of scenes, which has attracted widespread attention. Take the application of robot navigation as an example: In 1997, the Jet Propulsion Laboratory (JPL) developed a super-large field of view (horizontal field of view of 140°) binocular stereo vision obstacle detection system for the new generation of Mars rover, which can restore the scene dense depth map. The robots "Spirit" and "Opportunity" that successfully landed on Mars in 2004 also installed a set of stereo vision obstacle avoidance cameras (Hazard Avoid...

AI Technical Summary

Problems solved by technology

But the disadvantage is that there is a large distortion, and its distortion model no longer meets the conditions of perspective projection

With the expansion of the field of view, the angle of view becomes curved, the corresponding relationship between objects and images is complicated, and it is difficult to compensate

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