单目相机物体位姿估计方法研究
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摘要
现有的机器视觉通常以边缘轮廓和角点作为特征,因此要求背景单一,对环境结构化依赖程度高。为了拓展机器人的应用范围,使其脱离结构化的环境,提出了一种基于SIFT特征点和PNP技术的单目相机估计目标物体位姿的方法。以BumbleBee双目相机为硬件基础,以C++为开发平台,结合了Eigen计算库、OpenCV图像处理库和Triclops库,开发了单目视觉位姿估计算法,实现在复杂背景下对表面纹理较为丰富的物体的位姿估计。利用试验对所提方法进行了验证,试验结果表明,该算法具有较高的估计精度,可以作为机器抓取的依据。
The traditional methods of machine vision are usually characterized by edge contour and corner points, so it requires a monotonous background and relies on the structured environment strongly. In order to expand the application scope of robot and be away from structured environment, a method based on SIFT(Scale-Invariant Feature Transform) feature points and PNP(Perspective-n-Point) technique for monocular camera estimation of object position and pose is proposed. Taking the BumbleBee binocular camera as the hardware basis, C++ as the development platform, combined with Eigen computing library, OpenCV image processing library and Triclops library, the monocular vision pose estimation algorithm is developed to realize the pose estimation of objects with rich texture under complex background. The proposed method is verified by experiments, and the experiments results shows that the method can estimate with high accuracy and can be used as a basis for robot grasp.
The traditional methods of machine vision are usually characterized by edge contour and corner points, so it requires a monotonous background and relies on the structured environment strongly. In order to expand the application scope of robot and be away from structured environment, a method based on SIFT(Scale-Invariant Feature Transform) feature points and PNP(Perspective-n-Point) technique for monocular camera estimation of object position and pose is proposed. Taking the BumbleBee binocular camera as the hardware basis, C++ as the development platform, combined with Eigen computing library, OpenCV image processing library and Triclops library, the monocular vision pose estimation algorithm is developed to realize the pose estimation of objects with rich texture under complex background. The proposed method is verified by experiments, and the experiments results shows that the method can estimate with high accuracy and can be used as a basis for robot grasp.
引文
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[2] 逯建军,任晓军,孙伟,等.惯性/双目视觉里程计深组合导航方法[J].导航定位与授时,2016,3(3):37-43.Lu Jianjun,Ren Xiaojun,Sun Wei,et al.INS/Stereo visual odometry deeply integrated navigation method[J].Navigation Positioning & Timing,2016,3(3):37-43(in Chinese).
[3] 屈桢深,楚翔宇,赵霄洋,等.一种基于改进Kalman滤波的视觉/惯性组合导航算法[J].导航定位与授时,2017,4(2):14-20.Qu Zhenshen,Chu Xiangyu,Zhao Xiaoyang,et al.A visual/inertial integrated navigation algorithm based on improved Kalman filter[J].Navigation Positioning & Timing,2017,4(2):14-20(in Chinese).
[4] 王旒军,陈家斌,余欢,等.RGB-D SLAM综述[J].导航定位与授时,2017,4(6):9-18.Wang Yujun,Chen Jiabin,Yu Huan,et al.An overview of RGB-D SLAM[J].Navigation Positioning & Timing,2017,4(6):9-18(in Chinese).
[5] 张超,王芳,李楠.基于视觉的惯性导航误差在线修正[J].导航定位与授时,2018,5(3):104-110.Zhang Chao,Wang Fang,Li Nan.The online correction of IMU biases for visual-inertial navigation[J].Navigation Positioning & Timing,2018,5(3):104-110(in Chinese).
[6] Zhang Z .A flexible new technique for camera calibration[M].IEEE Computer Society,2000.
[7] Lowe D G.Distinctive image features from scale-invariant keypoints[J].International Journal of Computer Vision,2004,60(2):91-110.
[8] Rublee E,Rabaud V,Konolige K,et al.ORB:An efficient alternative to SIFT or SURF[C]// International Conference on Computer Vision.IEEE,2012:2564-2571.
[9] Bay H,Tuytelaars T,Van Gool L.SURF:Speeded up robust features[C]// European conference on Computer Vision.Springer,Berlin,Heidelberg,2006:404-417.
[10] Harris C G,Stephens M.A combined corner and edge detector[C]// Proceedings of 4th Alvey Vision Conference,1988:147-151.
[11] Gao X S,Hou X R,Tang J,et al.Complete solution classification for the perspective-three-point problem[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2003,25(8):930-943.
[12] Lepetit V,Moreno-Noguer F,Fua P.EPnP:An accurate O(n) solution to the PnP problem[J].International Journal of Computer Vision,2009,81(2):155-166.
[13] Hesch J A,Roumeliotis S I.A direct least-squares (DLS) method for PnP[C]// 2011 International Conference on Computer Vision.IEEE,2011:383-390.
[14] Fischler M A,Bolles R C.Random sample consensus:a paradigm for model fitting with applications to image analysis and automated cartography[J].Communications of the ACM,1981,24(6):381-395.
[15] Hu Z,Uchimura K.UV-disparity:an efficient algorithm for stereovision based scene analysis[C]// IEEE Intelligent Vehicles Symposium Proceedings.IEEE Computer Society,2005:48-54.