广东工业大学学报 ›› 2017, Vol. 34 ›› Issue (05): 40-44.doi: 10.12052/gdutxb.160122
池鹏可, 苏成悦
Chi Peng-ke, Su Cheng-yue
摘要: 将特征法与直接法相结合用于实现移动机器人的单目视觉里程计.采用Shi-Tomasi检测算法确保FAST检测算法提取的特征点的稳定性,通过四叉树算法使特征点分布均匀,并使用金字塔KLT算法跟踪下一帧图像的特征点.通过归一化直接线性变换DLT算法计算的单应矩阵重构相关位姿,利用最小化光度测量误差方法对特征点图像块进行直接匹配来估计移动机器人的运动位姿,并利用g2o库优化局部连续的运动位姿信息,提高运动估计的鲁棒性.通过TurtleBot移动机器人在室内环境下进行了实验验证,利用KITTI数据集测试本文设计的单目视觉里程计,实验结果表明本文所提方法的有效性和实用性.
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[1] 高云峰, 李伟超, 李建辉. 室内移动机器人视觉里程计研究[J]. 传感器与微系统, 2012, 31(2):26-29.GAO Y F, LI W C, LI J H. Research on visual odometry for indoor mobile robots[J]. Transducer and Microsystem Technologies, 2012, 31(2):26-29. [2] SCARAMUZZA D, FRAUNDORFER F. Visual odometry:Part I-the first 30 years and fundamentals[J]. IEEE Robotics & Automation Magazine, 2011, 18(4):80-92. [3] MAIMONE M, CHENG Y, MATTHIES L. Two years of visual odometry on the mars exploration rovers[J]. Journal of Field Robotics, 2007, 24(3):169-186. [4] WU K, DI K, SUN X, et al. Enhanced monocular visual odometry integrated with laser distance meter for astronaut navigation[J]. Sensors, 2014, 14(3):4981-5003. [5] 曾碧, 林展鹏, 邓杰航. 自主移动机器人走廊识别算法研究与改进[J]. 广东工业大学学报, 2016, 33(5):9-15.ZENG B, LIN Z P, DENG J H. Algorithm research on recognition and improvement for corridor of autonomous mobile robot[J]. Journal of Guangdong University of Technology, 2016, 33(5):9-15. [6] 孙伟, 钟映春, 谭志, 等. 多特征融合的室内场景分类研究[J]. 广东工业大学学报, 2015, 32(1):75-79.SUN W, ZHONG Y C, TAN Z, et al. Research on muti-featured fusion for indoor scene recognition[J]. Journal of Guangdong University of Technology, 2015, 32(1):75-79. [7] ENGEL J, SCHÖPS T, U D. LSD-SLAM:Large-scale direct monocular SLAM[C]//Computer Vision, European Conference. Switzerland:Springer, 2014:834-849. [8] NISTER D, NARODITSKY O, BERGEN J. Visual odometry[C]//Computer Vision and Pattern Recognition, IEEE Computer Society Conference. Washington D C:IEEE, 2004:652-659. [9] HENRY P, KRAININ M, HERBST E, et al. RGB-D mapping:Using Kinect-style depth cameras for dense 3D modeling of indoor environments[J]. The International Journal of Robotics Research, 2012, 31(5):647-663. [10] ZIENKIEWICZ J, LUKIERSKI R, DAVISON A. Dense, auto-calibrating visual odometry from a downward-looking camera[C]//British Machine vision Conference. Bristol:British Machine Vision Association, 2013:94. 1-94. 11. [11] 张毅, 童学容, 罗元. 一种改进SURF算法的单目视觉里程计[J]. 重庆邮电大学学报(自然科学版), 2014, 26(3):390-396.ZHANG Y, TONG X R, LUO Y. A novel monocular visual odometry method based on improved SURF algorithm[J]. Journal of Chongqing University of Posts and Telecommunications (Natural Science Edition), 2014, 26(3):390-396. [12] ROSTEN E, PORTER R, DRUMMOND T. Faster and better:a machine learning approach to corner detection[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2010, 32(1):105-119. [13] 姜静, 曹彦. 基于四叉树和特征融合的图像特征提取的研究[J]. 洛阳师范学院学报, 2014, 33(11):55-56,59.JIANG J, CAO Y. Research of image property extraction based on quadtree and property fusion[J]. Journal of Luoyang Normal University, 2014, 33(11):55-56,59. [14] BIRCHFIELD S. Derivation of Kanade-Lucas-Tomasi tracking equation[J]. Unpublished Notes, 1997, 44(5):1811-1843. [15] 孟繁雪. 非线性最小二乘问题的混合算法[D]. 上海:上海交通大学数学科学学院, 2011. [16] FAUGERAS O D, LUSTMAN F. Motion and structure from motion in a piecewise planar environment[J]. International Journal of Pattern Recognition & Artificial Intelligence, 1988, 2(3):485-508. [17] KUEMMERLE R, GRISETTI G, STRASDAT H, et al. g2o:A general framework for graph optimization[C]//Robotics and Automation, IEEE International Conference. Shanghai:IEEE, 2011:3607-3613. [18] BUTT R A, ALI S U. Semantic mapping and motion planning with turtlebot roomba[J]. Iop Conference Series:Materials Science & Engineering, 2013, 51(51):199-210. [19] 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. |
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