Passive Binocular Optical Motion Capture Technology Under Complex Illumination

doi:10.1007/s12204-023-2578-y

Abstract

Abstract: Passive optical motion capture technology is an effective mean to conduct high-precision pose estimation of small scenes of mobile robots; nevertheless, in the case of complex background and stray light interference in the scene, due to the influence of target adhesion and environmental reflection, this technology cannot estimate the pose accurately. A passive binocular optical motion capture technology under complex illumination based on binocular camera and fixed retroreflective marker balls has been proposed. By fixing multiple hemispherical retroreflective marker balls on a rigid base, it uses binocular camera for depth estimation to obtain the fixed position relationship between the feature points. After performing unsupervised state estimation without manual operation, it overcomes the influence of reflection spots in the background. Meanwhile, contour extraction and ellipse least square fitting are used to extract the marker balls with incomplete shape as the feature points, so as to solve the problem of target adhesion in the scene. A FANUC m10i-a robot moving with 6-DOF is used for verification using the above methods in a complex lighting environment of a welding laboratory. The result shows that the average of absolute position errors is 5.793mm, the average of absolute rotation errors is 1.997 ◦ , the average of relative position errors is 0.972mm, and the average of relative rotation errors is 0.002 ◦ . Therefore, this technology meets the requirements of high-precision measurement in a complex lighting environment when estimating the 6-DOF-motion mobile robot and has very significant application prospects in complex scenes.

Key words: complex scenes, pose estimation, binocular camera, fixed retroreflective target, least square fitting

摘要： 被动式光学运动捕捉技术是进行移动机器人小场景高精度位姿估计的有效手段，然而该技术在背景复杂及场景中有杂散光干扰的情况下，由于目标粘连及环境反光的影响，导致标志点的提取缺失、提取错误与匹配错误，无法进行准确的位姿估计。本文提出了一种基于双目相机和固定逆反射靶球的被动式光学运动捕捉技术，使用多个固定于刚性基座上呈半球形分布的逆反射靶球作为标志点，利用双目相机深度估计获取的标志点之间的固定位置关系，进行全流程全自动的移动机器人运动捕捉，克服背景中反射光点的影响。同时利用最小二乘轮廓拟合方法，提取不完整形状靶球作为标志点，解决场景中的目标粘连问题。利用以上方法在某焊接实验室复杂光照环境下使用FANUC m10i-a机器人六自由度运动进行验证，位姿估计的绝对定位误差平均值为5.793 mm，绝对角度误差平均值为1.997º，相对定位误差平均值为0.972 mm，相对角度误差平均值为0.002º。满足了在复杂光照环境下移动机器人六自由度高精度的测量要求，在复杂光照等实际场景中具有非常重要的应用价值。

关键词: 复杂场景，位姿估计，双目相机，固定逆反射靶球，最小二乘拟合

CLC Number:

TP242

Fu Yujia, Zhang Jian, Zhou Liping, Liu Yuanzhi, Qin Minghui, Zhao Hui, Tao Wei. Passive Binocular Optical Motion Capture Technology Under Complex Illumination[J]. J Shanghai Jiaotong Univ Sci, 2025, 30(2): 352-362.

References

[1] QIN T, LI P L, SHEN S J. VINS-mono: A robust and versatile monocular visual-inertial state estimator [J]. IEEE Transactions on Robotics, 2018, 34(4): 1004- 1020.
[2] SUN S Z, LI G Y, FENG Q Q, et al. Indoor positioning based on visible light communication and binocular vision [J]. Optics and Precision Engineering, 2020, 28(4): 834-843 (in Chinese).
[3] MENOLOTTO M, KOMARIS D S, TEDESCO S, et al. Motion capture technology in industrial applications: A systematic review [J]. Sensors, 2020, 20(19): 5687.
[4] EICHELBERGER P, FERRARO M, MINDER U, et al. Analysis of accuracy in optical motion capture - A protocol for laboratory setup evaluation [J]. Journal of Biomechanics, 2016, 49(10): 2085-2088.
[5] OptiTrack. Prepare setup area [EB/OL]. [2021-12- 16]. https://v30.wiki.optitrack.com/ index.php?title = Prepare Setup Area.
[6] SHAFIQ M S, T¨UMER S T, G¨ULER H C. Marker detection and trajectory generation algorithms for a multicamera based gait analysis system [J]. Mechatronics, 2001, 11(4): 409-437.
[7] KURIHARA K, HOSHINO S, YAMANE K, et al. Optical motion capture system with pan-tilt camera tracking and real time data processing [C]//2002 IEEE International Conference on Robotics and Automation. Washington: IEEE, 2002: 1241-1248.
[8] LI B, MENG Q, HOLSTEIN H. Articulated motion reconstruction from feature points [J]. Pattern Recognition, 2008, 41(1): 418-431.
[9] HUANG B S, CHEN FM, ZHANG J J. Optical motion capture system with improved algorithms [J]. Journal of Tongji University (Natural Science), 2005, 33(10): 1372-1376 (in Chinese).
[10] XIAO Z. Research and implementation of camera calibration and 3D reconstruction in optical motion capture system [D]. Changsha: Hunan University, 2018 (in Chinese).
[11] QIN Z B. Research on camera calibration technology based on one-dimensional calibration object [D]. Guangzhou: South China University of Technology, 2019 (in Chinese).
[12] VON GIOI R G, RANDALL G. A sub-pixel edge detector: an implementation of the canny/devernay algorithm [J]. Image Processing On Line, 2017, 7: 347-372.
[13] RAAJAN N R, RAMKUMAR M, MONISHA B, et al. Disparity estimation from stereo images [J]. Procedia Engineering, 2012, 38: 462-472.
[14] GUO J, ZHU C. Dynamic displacement measurement of large-scale structures based on the Lucas-Kanade template tracking algorithm [J]. Mechanical Systems and Signal Processing, 2016, 66: 425-436.
[15] DONG X L, YUAN J, HUANG S Z, et al. RGB-D visual odometry based on features of planes and line segments in indoor environments [J]. Robot, 2018, 40(6): 921-932 (in Chinese).
[16] GUO Q D, QUAN Y M, YU G P, et al. Improved binocular calibration based on ICP algorithm [J]. Acta Optica Sinica, 2016, 36(12): 191-198 (in Chinese).
[17] LEVENBERG K. A method for the solution of certain non-linear problems in least squares [J]. Quarterly of Applied Mathematics, 1944, 2(2): 164-168.
[18] STURM J, ENGELHARD N, ENDRES F, et al. A benchmark for the evaluation of RGB-D SLAM systems [C]//2012 IEEE/RSJ International Conference on Intelligent Robots and Systems. Vilamoura-Algarve: IEEE, 2012: 573-580.
[19] LI J Y, YANG B B, CHEN D P, et al. Survey and evaluation of monocular visual-inertial SLAM algorithms for augmented reality [J]. Virtual Reality & Intelligent Hardware, 2019, 1(4): 386-410.
[20] LIU Y, FU Y, CHEN F, et al. Simultaneous localization and mapping related datasets: a comprehensive survey [DB/OL]. (2021-10-16). https://arxiv. org/abs/2102.04036.
[21] GEIGER A, LENZ P, URTASUN R. Are we ready for autonomous driving? The KITTI vision benchmark suite [C]//2012 IEEE Conference on Computer Vision and Pattern Recognition. Providence: IEEE, 2012: 3354-3361.
[22] KUMMERLE R, GRISETTI G, STRASDAT H, et al. G2o: A general framework for graph optimization [C]//2011 IEEE International Conference on Robotics and Automation. Shanghai: IEEE, 2011: 3607-3613.
[23] UMEYAMA S. Least-squares estimation of transformation parameters between two point patterns [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1991, 13(4): 376-380.
[24] GRUPP M. EVO: Python package for the evaluation of odometry and SLAM [EB/OL]. [2021-12-16]. https://github.com/MichaelGrupp/evo.