3D Position Control of Robots Based on Distributed Real-time Vision Navigation System

Authors

1 Research Institute of Avionics, Isfahan University of Technology, Isfahan, Iran

2 Electrical & Computer Engineering Department, Isfahan University of Technology, Isfahan, Iran

Abstract

Indoor localization and navigation of mobile robots has remained as a challenging problem. Vision-based navigation systems have been utilized as a promising solution. But they come with deficiencies such as weak robustness against camera failures. In this paper a vision-based 3D localization and control method for aerial robots is proposed. This method addresses several key features such as higher precision, real-time processing, full area coverage, reduced camera costs and robustness against asynchronized images and camera failures. Most of the processes of the proposed method, including image preprocessing, object recognition and 2D target localization are carried out separately for each camera. Then the acquired data are fused by a distributed Kalman filter to estimate the 3D location of the robot. The method has been experimentally verified by controlling the indoor position of a small aerial robot.

Keywords


[1] P. D. Groves, “The complexity problem in future multi sensor navigation and positioning systems: A modular solution”, Journal of Navigation, vol. 67, no. 2, pp. 311-326, 2014.
[2] M. R. Hamrick, R. M. Ingman, inventors; At&T Intellectual Property I, LP, assignee. “GPS management system,” United States patent US 9,734,698, 2017.
[3] B. Zhang, Li. Zhigang, A. Perina, A. Del Bue, V. Murino and Liu. Jianzhuang, “Adaptive local movement modeling for robust object tracking,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 27, no. 7, pp. 1515-1526, 2017.
[4] Li, Juan. “A new efficient pose estimation and tracking method for personal devices: application to interaction in smart spaces,” PhD dissertation, Telecomunicacion, 2016.
[5] وحید آزادزاده، علی‌محمد لطیف، «دسته‌بندی ویژگی‌های استخراج شده از پیش‌زمینه و پس‌زمینه تصویر برای ردیابی اهداف متحرک هوایی»، مجله مهندسی برق دانشگاه تبریز، جلد 46، شماره 3، پاییز 1395.
[6] A. S. Huang, A. Bachrach, P. Henry, M. Krainin, D. Maturana, D. Fox, N. Roy, “Visual odometry and mapping for autonomous flight using an RGB-D camera,” In Robotics Research, pp. 235-252, 2017.
[7] J. Torres-Sospedra, A. Moreira, S. Knauth, R. Berkvens, R. Montoliu, O. Belmonte, S. Trilles, M. João Nicolau, F. Meneses, A. Costa and A. Koukofikis, “A realistic evaluation of indoor positioning systems based on Wi-Fi fingerprinting: The 2015 EvAAL–ETRI competition,” Journal of Ambient Intelligence and Smart Environments, vol. 9, no. 2, pp. 263-279, 2017.
[8] عقیل عبیری، محمدرضا محزون، «ردیابی اهداف متحرک هوایی با استفاده از تخمین چگالی کرنل بر اساس الگوریتم فیلتر ذره»، مجله مهندسی برق دانشگاه تبریز، جلد 45، شماره 3، پاییز 1394.
[9] H. Koyuncu, S. H. Yang, “A survey of indoor positioning and object locating systems,” IJCSNS International Journal of Computer Science and Network Security, vol. 10, no. 5, pp. 121-128, 2010.
[10] J. L. Crassidis, “Sigma-point Kalman filtering for integrated GPS and inertial navigation,” IEEE Transactions on Aerospace and Electronic Systems, vol. 42, no. 2, pp. 750-756, 2006.
[11] N. Abdelkrim, N. Aouf, A. Tsourdos, B. White, “Robust nonlinear filtering for INS/GPS UAV localization,” In Control and Automation, 2008 16th Mediterranean Conference on, pp. 695-702, 2008.
[12] H. Aoki, B. Schiele, A. Pentland, “Real time personal positioning system for a wearable computer,”  In Wearable Computers, 1999. Digest of Papers. The Third International Symposium on, pp. 37-43, 1999.
[13] A. Moemeni and E. Tatham, “Inertial-visual pose tracking using optical flow-aided particle filtering,” in IEEE Symposium on Computational Intelligence for Multimedia, Signal and Vision Processing (CIMSIVP), pp. 4001-4008, 2014.
[14] N. Sakagami and S. K. Choi, “Robust object tracking for underwater robots by integrating stereo vision, inertial and magnetic sensors,” In Proceedings of the ISCIE international symposium on stochastic systems theory and its applications, pp. 259-264, 2016.
[15] K. Satoh, S. Uchiyama, H. Yamamoto, “A head tracking method using bird's-eye view camera and gyroscope,” In Proceedings of the 3rd IEEE/ACM International Symposium on Mixed and Augmented Reality, pp. 202-211, 2004.
[16] P. Zhang, J. Gu, E. E. Milios, P. Huynh, “Navigation with IMU/GPS/digital compass with unscented Kalman filter,” In IEEE International Conference Mechatronics and Automation, 2005, vol. 3, pp. 1497-1502, 2005.
[17] J. Li, J. A. Besada, A. M. Bernardos, P. Tarrío, J. R. Casar, “A novel system for object pose estimation using fused vision and inertial data,” Information Fusion 33, pp. 15-28, 2017.
[18] Y. J. Lee, and A. Yilmaz, “Real-time object detection, tracking, and 3D positioning in a multiple camera setup”, The ISPRS Workshop on Image Sequence Analysis, vol. 55, p. 56, 2013.
[19] M. W. Park, I. Brilakis, “Construction worker detection in video frames for initializing vision trackers”, Automation in Construction, vol. 28, pp. 15 –25, 2012.
[20] J. Yang, O. Arif, P. A. Vela, J. Teizer, Z. Shi, “Tracking multiple workers on construction sites using video cameras”, Advanced Engineering Informatics, vol. 24, no. 4, pp. 428 –434, 2010.
[21] J. Teizer, P. A. Vela, “Personnel tracking on construction sites using video cameras”, Advanced Engineering Informatics, vol. 23, no. 4, pp. 452 –462, 2009.
[22] Li, Heng, G. Chan, J. K. W. Wong and M. Skitmore, “Real-time locating systems applications in construction” Automation in Construction, vol. 63, pp. 37-47, 2016.
[23] Y. Gu, A. Lo, I. Niemegeers, “A survey of indoor positioning systems for wireless personal networks”, IEEE Communications surveys & tutorials, vol. 11, no. 1, pp. 13-32, 2009.
[24] M. W. Park, C. Koch, I. Brilakis, “Three-dimensional tracking of construction resources using an on-site camera,” Journal of Computing in Civil Engineering, vol. 26, no. 4, pp. 541 –549, 2011.
[25] M. W. Park, A. Makhmalbaf, I. Brilakis, “Comparative study of vision tracking methods for tracking of construction site resources,”  Automation in Construction, vol. 20, no. 7, pp. 905 –915, 2011.
[26] M. S. Mahmoud, H. M. Khalid, “Distributed Kalman filtering: a bibliographic review”, IET Control Theory Appl., vol. 7, no. 4, pp. 483–501, 2013.
[27] A. Zulu, S. John, “A review of control algorithms for autonomous quadrotors,” arXiv preprint arXiv: 1602.02622, 2016.