Real-time stereoscopic vision implementation method based on FPGA

Abstract

The invention discloses a real-time stereoscopic vision implementation method based on FPGA. The implementation method operates in stereoscopic vision system which takes the FPGA as a core processing unit, has reasonable hardware structure, can meet the requirements of high-speed operation and high-seed transmission images, has multiple general interfaces and rich hardware resources, and can be further expanded and strengthened. Simultaneously a stereoscopic matching algorithm based on Census conversion is a non-parameterized matching method, is more robust for the conditions of non-uniform brightness, gain and the like, and improves the accuracy of the system. The invention adopts the reasonable hardware structure on the FPGA architecture, simultaneously implements a Census stereoscopic matching method by an ingenious logic design, and improves the accuracy and speed of stereoscopic vision implementation. Compared with the traditional method based on a general processor, the real-time stereoscopic vision implementation method utilizes parallel calculation and a reasonable production line design fully to map the algorithm on the structure, greatly improves the operating speed of the system, meets the requirements of high resolution, high accuracy and high speed and can be applicable to fields of autonomous vehicle navigation and the like.

Description

technical field [0001] The invention relates to the field of machine vision, in particular to an FPGA-based real-time stereoscopic vision realization method. Background technique [0002] The main task of the stereo vision system is to obtain the 3D information of the scene, and it has been widely used in the fields of mobile robots, target tracking, 3D reconstruction and so on. In the stereo vision system, stereo matching is the key core. Generally speaking, the scenes where mobile robots such as outdoor unmanned vehicles and lunar rovers usually lack regular features such as points and lines, and due to the uncertainty of the scene and the influence of factors such as lighting, feature extraction is often unstable, and only A sparse disparity map can be obtained, and a dense disparity map must be obtained through interpolation to reconstruct the scene. Therefore, the region matching algorithm is more inclined to be used in the real-time stereo vision system. [0003] In...

AI Technical Summary

Problems solved by technology

Although redundant calculations can be reduced through certain optimization techniques, the complexity can be reduced to O(N2D), but for general-purpose processors, it still cannot meet the real-time requirements, so it is necessary to use hardware acceleration technology or design dedicated hardware circuits

Images