Omnidirectional Stereo Vision Study from Vertical and Horizontal Stereo Configuration

  • Husein Aji Pratama Department of Informatics and Computer Engineering, Politeknik Elektronika Negeri Surabaya, Indonesia
  • Bima Sena Bayu Dewantara Department of Informatics and Computer Engineering, Politeknik Elektronika Negeri Surabaya, Indonesia
  • Dadet Pramadihanto Robotics and Intelligent System Center (RoISC), Politeknik Elektronika Negeri Surabaya, Indonesia
DOI: https://doi.org/10.24003/emitter.v10i2.700
Keywords: Stereo camera, Omnidirectional, Stereo matching, Stereo calibration, Omnidirectional calibration

Abstract

In stereo vision, an omnidirectional camera has high distortion compared to a standard camera, so the camera calibration is very decisive in its stereo matching. In this study, we will perform stereo matching for an omnidirectional camera with vertical and horizontal configuration so that the result of the image's depth has a 360-degree field of view by transforming the image using a calibration-based method. The result is that by using a vertical camera configuration, the image can be stereo matched directly, but by configuring a horizontal image, it is necessary to carry out a different stereo-matching process in each direction. Stereo matching with the semi-global matching method has better image results than block matching with more image objects detectable by the semi-global block matching method with a maximum disparity value of 32 pixels and with a window size of 21 pixels.

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References

Putra VCPH, Apriandy KI, Pramadihanto D, Barakbah AR, FLoW-Vision: Object Recognition and Pose Estimation System based on Three-Dimensional (3D) Computer Vision, 2021 International Electronics Symposium (IES), pp. 593–9, 2021. DOI: https://doi.org/10.1109/IES53407.2021.9593994

Dewantara BSB, Saputra RWA, Pramadihanto D, Estimating human body orientation from image depth data and its implementation, Machine Vision and Applications, vol. 33, no. 3, pp. 38, 2022. DOI: https://doi.org/10.1007/s00138-022-01290-1

Pratama AR, Dewantara BSB, Sari DM, Pramadihanto D, Density-based Clustering for 3D Stacked Pipe Object Recognition using Directly-given Point Cloud Data on Convolutional Neural Network, EMITTER International Journal of Engineering Technology. vol. 10, no. 1, pp. 153-169, 2022.

Islam A, Asikuzzaman Md, Khyam MO, Noor-A-Rahim Md, Pickering MR, Stereo Vision-Based 3D Positioning and Tracking, IEEE Access, vol. 8, pp. 138771–87, 2020. DOI: https://doi.org/10.1109/ACCESS.2020.3011360

Pramadihanto D, Alfarouq A, Waskitho SA, Sukaridhoto S, Merging of Depth Image Between Stereo Camera and Structure Sensor on Robot “FloW” Vision, International Journal on Advanced Science, Engineering and Information Technology, Vol. 7, no. 3, pp. 1014, 2017. DOI: https://doi.org/10.18517/ijaseit.7.3.2176

Shakeri A, Moshiri B, Garakani HG, Pedestrian Detection Using Image Fusion and Stereo Vision in Autonomous Vehicles, 2018 9th International Symposium on Telecommunications (IST), p. 592–6, 2018. DOI: https://doi.org/10.1109/ISTEL.2018.8661069

Song W, Yang Y, Fu M, Li Y, Wang M, Lane Detection and Classification for Forward Collision Warning System Based on Stereo Vision. IEEE Sensors J. vol. 18, no. 12, pp. 5151–63, 2018. DOI: https://doi.org/10.1109/JSEN.2018.2832291

Rodríguez-Quiñonez JC, Sergiyenko O, Flores-Fuentes W, Rivas-lopez M, Hernandez-Balbuena D, Rascón R, et al, Improve a 3D distance measurement accuracy in stereo vision systems using optimization methods’ approach, Opto-Electronics Review. vol. 25, no. 1, pp. 24–32, 2017. DOI: https://doi.org/10.1016/j.opelre.2017.03.001

Hong S, Chung D, Kim J, Kim Y, Kim A, Yoon HK, In‐water visual ship hull inspection using a hover‐capable underwater vehicle with stereo vision. J Field Robotics, vol. 36, no. 3, pp. 531–46, 2019. DOI: https://doi.org/10.1002/rob.21841

Lodi Rizzini D, Kallasi F, Aleotti J, Oleari F, Caselli S, Integration of a stereo vision system into an autonomous underwater vehicle for pipe manipulation tasks, Computers & Electrical Engineering, vol. 58, pp. 560–71, 2017. DOI: https://doi.org/10.1016/j.compeleceng.2016.08.023

AlQahtani F, Banks J, Chandran V, Zhang J, Three-Dimensional Head Pose Estimation Using a Stereo Camera Arrangement, Proceedings of the International Conference on Machine Vision and Applications - ICMVA, p. 28–35, 2018. DOI: https://doi.org/10.1145/3220511.3220522

Gong X, Lv Y, Xu X, Wang Y, Li M, Pose Estimation of Omnidirectional Camera with Improved EPnP Algorithm, Sensors, vol. 21, no. 12, 2021. DOI: https://doi.org/10.3390/s21124008

Morbidi F, Caron G, Phase Correlation for Dense Visual Compass From Omnidirectional Camera-Robot Images, IEEE Robot Autom Lett, vol. 2, no. 2, pp. 688–95, 2017. DOI: https://doi.org/10.1109/LRA.2017.2650150

Karaimer HC, Baris I, Bastanlar Y, Detection and classification of vehicles from omnidirectional videos using multiple silhouettes. Pattern Anal Applic, vol. 20, no. 3, pp. 893–905, 2017. DOI: https://doi.org/10.1007/s10044-017-0593-z

Dias Pais G, Dias TJ, Nascimento JC, Miraldo P, OmniDRL: Robust Pedestrian Detection using Deep Reinforcement Learning on Omnidirectional Cameras, 2019 International Conference on Robotics and Automation (ICRA), p. 4782–9, 2019. DOI: https://doi.org/10.1109/ICRA.2019.8794471

Patruno C, Colella R, Nitti M, Renò V, Mosca N, Stella E. A Vision-Based Odometer for Localization of Omnidirectional Indoor Robots. Sensors. 2020 Feb 6;20(3):875. DOI: https://doi.org/10.3390/s20030875

Liu, Guo, Feng, Yang, Accurate and Robust Monocular SLAM with Omnidirectional Cameras, Sensors, vol. 19, no. 20, pp. 4494, 2019. DOI: https://doi.org/10.3390/s19204494

Santosa SR, Dewantara BSB, Wibowo IK, Ball Detection Under Variety Lighting using Omnidirectional Camera, 2019 International Electronics Symposium (IES), p. 235–40, 2019. DOI: https://doi.org/10.1109/ELECSYM.2019.8901617

Nurrohmah EA, Sena Bayu B, Bachtiar MM, Kurnianto Wibowo I, Adryantoro R, Detecting Features of Middle Size Soccer Field using Omnidirectional Camera for Robot Soccer ERSOW. 2020 International Conference on Smart Technology and Applications (ICoSTA), p. 1–6, 2020. DOI: https://doi.org/10.1109/ICoSTA48221.2020.1570615971

Xie S, Lai PK, Laganiere R, Lang J, Effective Convolutional Neural Network Layers in Flow Estimation for Omni-Directional Images, 2019 International Conference on 3D Vision (3DV), p. 671–80, 2019. DOI: https://doi.org/10.1109/3DV.2019.00079

Kundu AS, Mazumder O, Dhar A, Lenka PK, Bhaumik S, Scanning Camera and Augmented Reality Based Localization of Omnidirectional Robot for Indoor Application, Procedia Computer Science, vol. 105, pp. 27–33, 2017. DOI: https://doi.org/10.1016/j.procs.2017.01.183

Gao W, Shen S, Dual-fisheye omnidirectional stereo, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), p. 6715–22, 2017. DOI: https://doi.org/10.1109/IROS.2017.8206587

Kallwies J, Engler T, Forkel B, Wuensche H-J, Triple-SGM: Stereo Processing using Semi-Global Matching with Cost Fusion, 2020 IEEE Winter Conference on Applications of Computer Vision (WACV), p. 192–200, 2020. DOI: https://doi.org/10.1109/WACV45572.2020.9093372

Chen M, Tang Y, Zou X, Huang Z, Zhou H, Chen S, 3D global mapping of large-scale unstructured orchard integrating eye-in-hand stereo vision and SLAM, Computers and Electronics in Agriculture, vol. 187, 2021. DOI: https://doi.org/10.1016/j.compag.2021.106237

McGuire K, de Croon G, De Wagter C, Tuyls K, Kappen H, Efficient Optical Flow and Stereo Vision for Velocity Estimation and Obstacle Avoidance on an Autonomous Pocket Drone, IEEE Robot Autom Lett. vol. 2, no. 2, pp. 1070–6, 2017. DOI: https://doi.org/10.1109/LRA.2017.2658940

Scaramuzza D, Martinelli A, Siegwart R. A Flexible Technique for Accurate Omnidirectional Camera Calibration and Structure from Motion, Fourth IEEE International Conference on Computer Vision Systems (ICVS’06), p. 45–45, 2006. DOI: https://doi.org/10.1109/ICVS.2006.3

Gong X, Lv Y, Xu X, Jiang Z, Sun Z, High-Precision Calibration of Omnidirectional Camera Using an Iterative Method, IEEE Access, vol. 7, 152179–86, 2019. DOI: https://doi.org/10.1109/ACCESS.2019.2945635

Lin C-H, Chou W-J, Acceleration of the transformation from elliptic omnidirectional images to panoramic images using graphic processing units, 2016 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW), p. 1–2, 2016. DOI: https://doi.org/10.1109/ICCE-TW.2016.7520975

Kallwies J, Wuensche H-J, Effective Combination of Vertical and Horizontal Stereo Vision, 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), p. 1992–2000, 2018. DOI: https://doi.org/10.1109/WACV.2018.00220

Scaramuzza D, Martinelli A, Siegwart R, A Toolbox for Easily Calibrating Omnidirectional Cameras, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, p. 5695–701, 2006. DOI: https://doi.org/10.1109/IROS.2006.282372

Published
2022-12-28
How to Cite
Husein Aji Pratama, Bima Sena Bayu Dewantara, & Dadet Pramadihanto. (2022). Omnidirectional Stereo Vision Study from Vertical and Horizontal Stereo Configuration. EMITTER International Journal of Engineering Technology, 10(2), 294-310. https://doi.org/10.24003/emitter.v10i2.700
Issue
Vol 10 No 2 (2022)
Section
Articles

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