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Wang Tianyu, Dong Wenbo, Wang Zhenyu. Position and orientation measurement system based on monocular vision and fixed target[J]. Infrared and Laser Engineering, 2017, 46(4): 427003-0427003(8). doi: 10.3788/IRLA201746.0427003
Citation: Wang Tianyu, Dong Wenbo, Wang Zhenyu. Position and orientation measurement system based on monocular vision and fixed target[J]. Infrared and Laser Engineering, 2017, 46(4): 427003-0427003(8). doi: 10.3788/IRLA201746.0427003
shu

Position and orientation measurement system based on monocular vision and fixed target

doi: 10.3788/IRLA201746.0427003
  • 1.

    School of Control and Computer Engineering,North China Electric Power University,Beijing 102206,China;

  • 2.

    Technology and Engineering Center for Space Utilization,Chinese Academy of Sciences,Beijing 100094,China;

  • 3.

    Key Laboratory for Space Utilization,Chinese Academy of Sciences,Beijing 100094,China

  • Received Date: 2016-08-05
  • Rev Recd Date: 2016-09-10
  • Publish Date: 2017-04-25
  • The position and orientation measurement system based on computer vision is extensively applied on robotics, motion control and precision detection systems. Using the minimum hardware resource, a localization system based on mono-vision and manual planar target was designed and the method of image matching and position resolving was also studied. Firstly, the object detection based on image matching was used to get the coordinate of the planar target in image. The matching was based on SIFT features and projection estimation to detect the target in image, and then the accurate coordinate of the target's center was calculated by some inherent shape information. A number of image samples were used to validate the accuracy and robustness of the image matching algorithm. Secondly, a new method to solve the PnP problem based on the rectangular distribution was proposed. The method uses the locations of target control points in the image coordinate system and the object coordinate system was used to get the relative position and orientation between the moving object and the camera. The experiment was conducted on the 5D precision displacement stage and results show that the system can achieve the accuracy level of mm in the range of 800 mm, which meets the project requirement.
  • [1] Gao Wen, Zhu Ming, He Baigen, et al. Overview of target tracking technology[J]. Chinese Optics, 2014, 7(3):365-375. (in Chinese)高文, 朱明, 贺柏根, 等.目标跟踪技术综述[J]. 中国光学, 2014, 7(3):365-375.
    [2] Wang Zhiqiang, Cheng Hong, Yang Guang, et al. Fast target location method of global image registration[J]. Infrared and Laser Engineering, 2015, 44(S1):225-229. (in Chinese)王志强, 程红, 杨桄, 等. 全局图像配准的目标快速定位方法[J]. 红外与激光工程, 2015, 44(S1):225-229.
    [3] Mikolajczyk K, Schmid C. A performance evaluation of local descriptors[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(10):1615-1630.
    [4] Lowe D G. Distinctive image features from scale invariant key pionts[J]. International Journal of Computer Vision, 2004, 60(2):91-110.
    [5] Wang Rui, Zhu Zhengdan. SIFT matching with color invariant characteristics and global context[J]. Optics and Precision Engineering, 2015, 23(1):295-301. (in Chinese)王睿, 朱正丹.融合全局-颜色信息的尺度不变特征变换[J]. 光学精密工程, 2015, 23(1):295-301.
    [6] Cui Xiongwen, Wu Qinzhang, Jiang Ping, et al. Affine-invariant target tracking based on subspace representation[J].Infrared and Laser Engineering, 2015, 44(2):769-774. (in Chinese)崔雄文, 吴钦章, 蒋平, 等.子空间模型下的仿射不变目标跟踪[J]. 红外与激光工程, 2015, 44(2):769-774.
    [7] Liu Chang, Zhu Feng, Xia Renbo. Monocular pose determination from coplanar two points and one line features[J]. Application Research of Computers, 2012, 29(8):3145-3147. (in Chinese)刘昶, 朱枫, 夏仁波. 基于共面二点一线特征的单目视觉定位[J]. 计算机应用研究, 2012, 29(8):3145-3147.
    [8] Liu Yinxiao, Wei Wei. Target points positioning algorithm based on rare information in uncalibrated camera's photos[J].Application Research of Computers, 2015, 32(1):272-275. (in Chinese)刘吟啸, 韦巍.基于极少信息的任意照片目标点定位算法[J]. 计算机应用研究, 2015, 32(1):272-275.
    [9] Xiao Xizhen, Li Wentao, Tang Yandong, et al. Position-pose measurement of rectangle with unknown aspect ratio based on nonlinear optimization[J]. Information and Control, 2016, 45(1):108-113. (in Chinese)肖锡臻, 李文涛, 唐延东, 等.基于非线性优化的长宽比未知矩形的位姿测量[J]. 信息与控制, 2016, 45(01):108-113.
    [10] Li Ying, Ye Peijian, Peng Jing, et al. Artificial target recognition and location based on Mars exploration[J].Optics and Precision Engineering, 2015, 23(2):566-572. (in Chinese)李莹, 叶培建, 彭兢, 等.基于火星探测的人工标志识别定位[J]. 光学精密工程, 2015, 23(2):566-572.
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Position and orientation measurement system based on monocular vision and fixed target

doi: 10.3788/IRLA201746.0427003
  • 1. School of Control and Computer Engineering,North China Electric Power University,Beijing 102206,China;
  • 2. Technology and Engineering Center for Space Utilization,Chinese Academy of Sciences,Beijing 100094,China;
  • 3. Key Laboratory for Space Utilization,Chinese Academy of Sciences,Beijing 100094,China
  • Received Date: 2016-08-05
  • Rev Recd Date: 2016-09-10
  • Publish Date: 2017-04-25

Abstract: The position and orientation measurement system based on computer vision is extensively applied on robotics, motion control and precision detection systems. Using the minimum hardware resource, a localization system based on mono-vision and manual planar target was designed and the method of image matching and position resolving was also studied. Firstly, the object detection based on image matching was used to get the coordinate of the planar target in image. The matching was based on SIFT features and projection estimation to detect the target in image, and then the accurate coordinate of the target's center was calculated by some inherent shape information. A number of image samples were used to validate the accuracy and robustness of the image matching algorithm. Secondly, a new method to solve the PnP problem based on the rectangular distribution was proposed. The method uses the locations of target control points in the image coordinate system and the object coordinate system was used to get the relative position and orientation between the moving object and the camera. The experiment was conducted on the 5D precision displacement stage and results show that the system can achieve the accuracy level of mm in the range of 800 mm, which meets the project requirement.

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