Uniform distributed subpixel ORB feature extraction method for high-precision SLAM

Yi ZHANG; Gang-wu JIANG; Ying YU; Yuan ZHOU

doi:10.3788/OPE.20182610.2575

您当前的位置：

首页 >

文章列表页 >

Uniform distributed subpixel ORB feature extraction method for high-precision SLAM

Information Sciences | 更新时间：2020-07-05

- Uniform distributed subpixel ORB feature extraction method for high-precision SLAM
- Optics and Precision Engineering Vol. 26, Issue 10, Pages: 2575-2583(2018)
- 作者机构：
  
  1.信息工程大学, 河南郑州 450001
  2.陆军炮兵防空兵学院南京校区, 江苏南京 211132
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20182610.2575
  CLC： TP242.6
- Received：09 January 2018，
  
  Accepted：23 March 2018，
  
  Published：25 October 2018
- 稿件说明：
移动端阅览
Yi ZHANG, Gang-wu JIANG, Ying YU, et al. Uniform distributed subpixel ORB feature extraction method for high-precision SLAM[J]. Optics and precision engineering, 2018, 26(10): 2575-2583.
DOI：

Yi ZHANG, Gang-wu JIANG, Ying YU, et al. Uniform distributed subpixel ORB feature extraction method for high-precision SLAM[J]. Optics and precision engineering, 2018, 26(10): 2575-2583. DOI： 10.3788/OPE.20182610.2575.

摘要

在视觉同时定位与地图构建问题中，ORB（Oriented FAST and Rotated BRIEF）特征由于其高效、稳定的优点而受到广泛关注。针对ORB特征提取过程中存在的像点量测精度较低、特征聚集现象明显等问题，提出了一种适用于高精度SLAM的均衡化亚像素ORB特征提取方法。分析了精确特征定位的原理，对误差方程进行合理的简化并采用一种基于模板窗口距离的权函数计算方法，大幅降低了计算负担；设计了一种基于四叉树结构的特征均衡化方案，对包含特征的像平面空间进行有限次数的迭代分割，然后选取具有最优响应的特征。试验表明，本文方法进行特征提取的额外计算负担小于2.5 ms，在运行TUM和KITTI数据集时，ORB特征的量测精度分别为0.84和0.62 Pixel，达到亚像素水平，可以降低误差初值，提高光束法平差效率，并能够在满足特征总体分布规律的情况下，显著改善特征聚集的现象，有利于后续问题的稳健、准确求解。

Abstract

In visual SLAM problems

the ORB feature has drawn much attention because of its high efficiency and stability. To address problems such as the low accuracy of image point measurements and the obvious phenomenon of feature aggregation during ORB feature extraction

a uniform distributed subpixel ORB feature extraction method suitable for high-precision SLAM was proposed. In this study

the principle of precise feature positioning was first analyzed

the error equation was then reasonably simplified

and a weight function calculation method based on template window distance was finally adopted

all of which significantly reduce the algorithm's computational cost. A quadtree-based uniform distribution solution was designed in which the image plane space is segmented with only a limited number of iterations. Features with optimal response are then exported. Experiments show that the additional computational burden of feature extraction for our method is less than 2.5 ms. The measurement accuracy of ORB features is 0.84 and 0.62 pixels on the TUM and KITTI datasets

respectively

reaching the subpixel level. Our method can thus reduce the initial value of errors and increase the efficiency of bundle adjustment. The problem of feature aggregation is effectively solved based on the condition of satisfying the overall distribution of features

which is beneficial to the robust and accurate solution of subsequent problems.

关键词

Keywords

references

NISTÉR D, NARODITSKY O, BERGEN J. Visual odometry[J]. Proceedings of the 2004 IEEE Computer Society Conference on, IEEE, 2004, 1:652-659.

吴修振, 刘刚, 于凤全, 等.基于单目视觉的GPS辅助相机外参数标定[J].光学精密工程, 2017, 25(8):2252-2258.

WU X Z, LIU G, YU F Q, et al.. Calibration of camera extrinsic parameters based on monocular visual with GPS assistant[J]. Opt. Precision Eng., 2017, 25(8):2252-2258. (in Chinese)

LOWE D G. Distinctive image features from scale-invariant keypoints[J]. International journal of computer vision, 2004, 60(2):91-110.

顾照鹏, 刘宏.单目视觉同步定位与地图创建方法综述[J].智能系统学报, 2015, 10(4):499-507.

GU Z P, LIU H. A survey of monocular simultaneous localization and mapping[J]. CAAI Transactions on Intelligent Systems, 2015, 10(4):499-507. (in Chinese)

RUBLEE E, RABAUD V, KONOLIGE K, et al.. ORB: An efficient alternative to SIFT or SURF[C]// Computer Vision (ICCV), 2011 IEEE international conference on, IEEE , 2011: 2564-2571. http://dl.acm.org/citation.cfm?id=2356268

FUENTES-PACHECO J, RUIZ-ASCENCIO J, RENDÓN-MANCHA J M. Visual simultaneous localization and mapping:a survey[J]. Artificial Intelligence Review, 2015, 43(1):55-81.

曹天扬, 蔡浩原, 方东明, 等.结合图像内容匹配的机器人视觉导航定位与全局地图构建系统[J].光学精密工程, 2017, 25(8):2221-2232.

CAO T Y, CAI H Y, FANG D M, et al.. Robot vision system for keyframe global map establishment and robot localization based on graphic content matching[J]. Opt. Precision Eng., 2017, 25(8):2221-2232. (in Chinese)

李劲澎, 姜挺, 肖达, 等.基于关系图的无人机影像三维重建[J].光学精密工程, 2016, 24(6):1501-1509.

LI J P, JIANG T, XIAO D, et al.. On diagram-based three-dimensional reconstruction of UAV image[J]. Opt. Precision Eng., 2016, 24(6):1501-1509.(in Chinese)

DA F, ZHANG H. Sub-pixel edge detection based on an improved moment[J]. Image & Vision Computing, 2010, 28(12):1645-1658.

HERMOSILLA T, BERMEJO E, BALAGUER A, et al.. Non-linear fourth-order image interpolation for subpixel edge detection and localization[J]. Image & Vision Computing, 2008, 26(9):1240-1248.

李喆, 丁振良, 袁峰.基于分层插值和最小二乘拟合的亚像素细分算法[J].南京理工大学学报(自然科学版), 2008, 32(5):615-618.

LI Z, DING Z L, YUAN F. Subpixel algorithm based on level interpolation and least squares fitting[J].Journal of Nanjing University of Science and Technology:Natural Science, 2008, 32(5):615-618. (in Chinese)

陈小卫, 徐朝辉, 郭海涛, 等.利用极值梯度的通用亚像素边缘检测方法[J].测绘学报, 2014(5):500-507.

CHEN X W, XU Z H, GUO H T, et al..Universal subpixel edge detection algorithm based on extremal gradient[J]. Acta Geodaetica et Cartographica Sinica, 2014, 43(5):500-507. (in Chinese)

ROSTEN E, DRUMMOND T. Machine learning for high-speed corner detection[C]. ECCV, Graz, Austria , 2006: 430-443. http://www.springerlink.com/content/y11g42n05q626127

ROSTEN E, PORTER R, DRUMMOND T. Faster and better:a machine learning approach to corner detection[J]. Pattern Anal Mach Intell, IEEE Trans, 2010, 32(1):105-119.

ROSIN PL. Measuring Corner Properties[J]. Computer Vision and Image Understanding, 1999, 73(2):291-307.

王永明, 王贵锦.图像局部不变性特征与描述[M].北京:国防工业出版社, 2010:37-38.

WANG Y M, WANG J G. Local invariance features and description of images[M]. Beijing:National Defense Industry Press, 2010:37-38.(in Chinese)

李劲澎, 姜挺, 龚志辉, 等.稳健李代数旋转平均用于GPS辅助无人机影像三维重建[J].光学精密工程, 2017, 25(6):1607-1618.

LI J P, JIANG T, GONG Z H, et al.. Gps-support 3d reconstruction of UAV images based on robust lie-algebraic rotation averaging[J]. Opt. Precision Eng., 2017, 25(6):1607-1618. (in Chinese)

MUR-ARTAL R, MONTIEL J M M, TARDÓS J D. Orb-slam:a versatile and accurate monocular slam system[J]. IEEE Transactions on Robotics, 2015, 31(5):1147-1163.

MUR-ARTAL R, TARDÓS J D. ORB-SLAM2:An Open-Source SLAM System for Monocular, Stereo, and RGB-D Cameras[J]. IEEE Transactions on Robotics, 2017, 33(5):1255-1262.

STURM J, ENGELHARD N, ENDRES F, et al.. A benchmark for the evaluation of RGB-D SLAM systems[C]// Ieee / rsj International Conference on Intelligent Robots and Systems, IEEE , 2012: 573-580. http://ieeexplore.ieee.org/xpls/icp.jsp?arnumber=6385773

GEIGER A, LENZ P, STILLER C, et al.. Vision meets robotics:The KITTI dataset[J]. International Journal of Robotics Research, 2013, 32(11):1231-1237.

Views

240

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Deep learning target detection based on pre-segmentation and regression

Related Author

PAN Rong

SUN Wei

Related Institution

School of Aerospace Science and Technology, Xidian University, Xi'an 710071, China

AI问答

Address：No.3888 Dong Nanhu Road, Changchun, Jilin, China Postal code：130033
Tel：0431-86176855 Email：gxjmgc@ciomp.ac.cn
Technical support is provided by Beijing Founder electronics co., LTD 吉ICP备11002662号-17 京公网安备11010802024621
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰