Improve three-dimensional point localization accuracy in stereo vision systems using a novel camera calibration method

Computer vision systems have demonstrated to be useful in applications of autonomous navigation, especially with the use of stereo vision systems for the three-dimensional mapping of the environment. This article presents a novel camera calibration method to improve the accuracy of stereo vision systems for three-dimensional point localization. The proposed camera calibration method uses the least square method to model the error caused by the image digitalization and the lens distortion. To obtain particular three-dimensional point coordinates, the stereo vision systems use the information of two images taken by two different cameras. Then, the system locates the two-dimensional pixel coordinates of the three-dimensional point in both images and coverts them into angles. With the obtained angles, the system finds the three-dimensional point coordinates through a triangulation process. The proposed camera calibration method is applied in the stereo vision systems, and a comparative analysis between the real and calibrated three-dimensional data points is performed to validate the improvements. Moreover, the developed method is compared with three classical calibration methods to analyze their advantages in terms of accuracy with respect to tested methods.

[1]  Tianjiang Hu,et al.  Ground Stereo Vision-Based Navigation for Autonomous Take-off and Landing of UAVs: A Chan-Vese Model Approach , 2016 .

[2]  Wendy Flores-Fuentes,et al.  A methodological use of inertial navigation systems for strapdown navigation task , 2017, 2017 IEEE 26th International Symposium on Industrial Electronics (ISIE).

[3]  B L Anderson,et al.  Stereoscopic occlusion and the aperture problem for motion: a new solution 1 A preliminary version of some of the experiments reported in this paper were presented at the 1994 ARVO conference, Sarasota, Florida. 1 , 1999, Vision Research.

[4]  Wendy Flores-Fuentes,et al.  Structural Health Monitoring based on Optical Scanning Systems and SVM , 2014, 2014 IEEE 23rd International Symposium on Industrial Electronics (ISIE).

[5]  Oleg Starostenko,et al.  Resolution improvement of dynamic triangulation method for 3D vision system in robot navigation task , 2010, IECON 2010 - 36th Annual Conference on IEEE Industrial Electronics Society.

[6]  O. Sergiyenko,et al.  Improve 3D laser scanner measurements accuracy using a FFBP neural network with Widrow-Hoff weight/bias learning function , 2014 .

[7]  Wendy Flores-Fuentes,et al.  Improve a 3D distance measurement accuracy in stereo vision systems using optimization methods’ approach , 2017 .

[8]  Oleg Sergiyenko,et al.  Surface recognition improvement in 3D medical laser scanner using Levenberg-Marquardt method , 2013, Signal Process..

[9]  L. Priya,et al.  Object recognition and 3D reconstruction of occluded objects using binocular stereo , 2017, Cluster Computing.

[10]  Tobias Doernbach,et al.  Underwater navigation using visual markers in the context of intervention missions , 2019 .

[11]  N. Bruce,et al.  Low-energy/pulse response and high-resolution-CMOS camera for spatiotemporal femtosecond laser pulses characterization @ 1.55 μm. , 2019, The Review of scientific instruments.

[12]  Antonios Gasteratos,et al.  A stereo matching approach based on particle filters and scattered control landmarks , 2015, Image Vis. Comput..

[13]  Darius Burschka,et al.  Advances in Computational Stereo , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  Oleg Starostenko,et al.  Optical 3D laser measurement system for navigation of autonomous mobile robot , 2014 .

[15]  Wendy Flores-Fuentes,et al.  Accuracy improvement in 3D laser scanner based on dynamic triangulation for autonomous navigation system , 2017, 2017 IEEE 26th International Symposium on Industrial Electronics (ISIE).

[16]  Eija Honkavaara,et al.  A Fisheye Image Matching Method Boosted by Recursive Search Space for Close Range Photogrammetry , 2019, Remote. Sens..

[17]  Phil F. Culverhouse,et al.  Automating Active Stereo Vision Calibration Process with Cobots , 2017 .

[18]  Kevin Cleary,et al.  Stereoscopic augmented reality for laparoscopic surgery , 2014, Surgical Endoscopy.

[19]  Zhengyou Zhang,et al.  A Flexible New Technique for Camera Calibration , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[20]  A. Kilpela,et al.  PULSED TIME-OF-FLIGHT LASER RANGE FINDER TECHNIQUES FOR FAST, HIGH PRECISION MEASUREMENT APPLICATIONS , 2004 .

[21]  Moises Rivas-Lopez,et al.  Obtención de Trayectorias Empleando el Marco Strapdown INS/KF: Propuesta Metodológica. , 2018, Revista Iberoamericana de Automática e Informática industrial.

[22]  Xiaolong Hui,et al.  Vision-based autonomous navigation approach for unmanned aerial vehicle transmission-line inspection , 2018 .

[23]  Tsutomu Maruyama,et al.  Real-Time Stereo Vision System: A Multi-Block Matching on GPU , 2018, IEEE Access.

[24]  Wei Liu,et al.  Improved camera calibration method based on perpendicularity compensation for binocular stereo vision measurement system. , 2015, Optics express.

[25]  Andrew W. Fitzgibbon,et al.  Simultaneous linear estimation of multiple view geometry and lens distortion , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[26]  Terrance L. Huntsberger,et al.  Stereo vision–based navigation for autonomous surface vessels , 2011, J. Field Robotics.

[27]  Xavier Armangué,et al.  A comparative review of camera calibrating methods with accuracy evaluation , 2002, Pattern Recognit..

[28]  Joaquim Salvi,et al.  Active Stereo-matching for One-shot Dense Reconstruction , 2012, ICPRAM.

[29]  Julio C. Rodríguez-Quiñonez,et al.  Implementing k-Nearest Neighbor Algorithm on Scanning Aperture for Accuracy Improvement , 2018, IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society.

[31]  Hak Kyeong Kim,et al.  A Vision System for Identifying Structural Vibration in Civil Engineering Constructions , 2006, 2006 SICE-ICASE International Joint Conference.

[32]  Gareth Bradshaw.  Non-Contact Surface Geometry Measurement Techniques , 1999 .

[33]  Daniel Martín Carabias,et al.  Sistema de visión estereoscópica para navegación autónoma de vehículos no tripulados , 2010 .

[34]  Ahmad Fairuz Omar,et al.  A Review: Photonic Devices Used for Dosimetry in Medical Radiation , 2019, Sensors.

[35]  Olivier D. Faugeras,et al.  Complete Dense Stereovision Using Level Set Methods , 1998, ECCV.

[36]  Robin De Keyser,et al.  The development of an autonomous navigation system with optimal control of an UAV in partly unknown indoor environment , 2018 .

[37]  Quanzeng Wang,et al.  Development of the local magnification method for quantitative evaluation of endoscope geometric distortion , 2016, Journal of biomedical optics.

[38]  Qing Ma,et al.  Improving stereo matching algorithm with adaptive cross-scale cost aggregation , 2018 .

[39]  A. A. Bykov,et al.  A miniature prism-based stereoscopic system for 3D machine vision applications , 2019, International Conference on Machine Vision.

[40]  Antonio Fernández-Caballero,et al.  Conceptos y Técnicas de Estereovisión por Computador , 2006 .

[41]  Pratibha Mishra,et al.  Advanced Engineering Mathematics , 2013 .

[42]  E. L. Lehmann,et al.  Theory of point estimation , 1950 .

[43]  Julio C. Rodriguez-Quinonez,et al.  Optical monitoring of scoliosis by 3D medical laser scanner , 2014 .

[44]  Naim Dahnoun,et al.  Real-time stereo vision-based lane detection system , 2018, ArXiv.

[45]  Michael G. Strintzis,et al.  Stereo vision system for precision dimensional inspection of 3D holes , 2003, Machine Vision and Applications.

[46]  S. Rodriguez Palma,et al.  Simultaneous calibration of stereo vision and 3D optical tracker for robotic microsurgery , 2012, 2012 38th Annual Northeast Bioengineering Conference (NEBEC).

[47]  Fuqiang Zhou,et al.  Precise calibration of binocular vision system used for vision measurement. , 2014, Optics express.

[48]  Phil F. Culverhouse,et al.  Active stereo platform: online epipolar geometry update , 2018, EURASIP J. Image Video Process..

[49]  Yi Zhou,et al.  Semi-Dense 3D Reconstruction with a Stereo Event Camera , 2018, ECCV.

[50]  O. Y. Sergiyenko,et al.  Optoelectronic 3D laser scanning technical vision system based on dynamic triangulation , 2012, IEEE Photonics Conference 2012.

[51]  J. M. Hernández.  Técnicas de procesamiento de imágenes estereoscópicas , 2010 .