光子学报, 2011, 40 (8): 1225, 网络出版: 2011-08-29
基于广义正交迭代算法的立体视觉定位
Stereo Visual Localization Based on Generalized Orthogonal Iterative Algorithm
视觉定位 广义正交迭代算法 立体视觉 视觉导航 Visual localization Generalized orthogonal iterative algorithm Stereo vision Visual navigation
摘要
提出了一种新的基于广义正交迭代算法的立体视觉定位.该算法通过提取CenSurE局部特征和相应的U-SURF描述符,采用SAD方法进行子像素立体匹配,并利用U-SURF描述符匹配进行前后帧图像特征跟踪.在RANSAC框架下对匹配点进行3D-3D运动估计获得了运动参量的初始值.由于3D-3D运动估计使3D点集间欧式距离误差最小,而3D特征点坐标受噪音影响很大,因此运动估计误差也较大.本文把广义正交迭代算法应用到立体视觉定位方法中,得到使立体相机目标空间共线性误差最小的运动估计参量,由于共线性误差比3D-3D运动估计算法中的共点误差受噪音影响更小,从而大大较少了运动估计误差.仿真实验和户外真实实验表明: 本文算法获得了较高的计算准确度、鲁棒性和实时性,优于传统方法.
Abstract
A new stereo visual localization method was proposed based on generalized orthogonal iterative algorithm. Firstly, CensurE features and U-SURF descriptors were extracted, sub-pixel stereo matching were performed based on SAD method, and features between two consecutive image frames were matched using U-SURF descriptor. Then, 3D-3D motion estimation was carried out to obtain initial motion parameters in the framework of RANSAC. 3D-3D motion estimation could obtain the minimum error of Euclidean distance between 3D points. The 3D coordinates of feature points were greatly affected by noise, so the motion estimation error was large. In this paper, generalized orthogonal iterative algorithm was applied to visual stereo localization to obtain motion estimation parameters by minimising object-space collinearity error of points sensed by stereo cameras. The motion estimation error was greatly reduced because Euclidean distance error between 3D points was more affected by noise than collinearity error of points. Simulation experiment and outdoor intelligent vehicle experiment show that the proposed method can be run at real-time, and achieves a high accuracy and robustness, better than traditional methods.
许允喜, 蒋云良, 陈方. 基于广义正交迭代算法的立体视觉定位[J]. 光子学报, 2011, 40(8): 1225. XU Yun-xi, JIANG Yun-liang, CHEN Fang. Stereo Visual Localization Based on Generalized Orthogonal Iterative Algorithm[J]. ACTA PHOTONICA SINICA, 2011, 40(8): 1225.