A Low-Cost Vision-Based Unmanned Aerial System for Extremely Low-Light GPS-Denied Navigation and Thermal Imaging
This paper presents the design and implementation
details of a complete unmanned aerial system (UAS) based
on commercial-off-the-shelf (COTS) components, focusing on
safety, security, search and rescue scenarios in GPS-denied
environments. In particular, The aerial platform is capable
of semi-autonomously navigating through extremely low-light,
GPS-denied indoor environments based on onboard sensors only,
including a downward-facing optical flow camera. Besides, an
additional low-cost payload camera system is developed to stream
both infra-red video and visible light video to a ground station in
real-time, for the purpose of detecting sign of life and hidden humans.
The total cost of the complete system is estimated to be $1150,
and the effectiveness of the system has been tested and validated
in practical scenarios.
[1] A. Bachrach and S. Prentice, “RANGE Robust autonomous navigation
in GPS denied environments,” Journal of Field Robotics, 2011.
[2] A. Bry, “State estimation for aggressive flight in GPS-denied
environments using onboard sensing,” International Conference on
Robotics and Automation, no. Icra, pp. 1–8, May 2012.
[3] J. Engel, J. Sturm, and D. Cremers, “Camera-based navigation of a
low-cost quadrocopter,” Intelligent Robots and Systems, pp. 2815–2821,
Oct. 2012.
[4] S. Shen, Y. Mulgaonkar, N. Michael, and V. Kumar, “Vision-Based
State Estimation and Trajectory Control Towards High-Speed Flight
with a Quadrotor.” Robotics: Science and Systems, 2013.
[5] E. Jones and S. Soatto, “Visual-inertial navigation, mapping and
localization: A scalable real-time causal approach,” The International
Journal of Robotics, pp. 1–38, 2011.
[6] S. Weiss, D. Scaramuzza, and R. Siegwart, “Monocular SLAM
based navigation for autonomous micro helicopters in GPS-denied
environments,” Journal of Field Robotics, vol. 28, no. 6, pp. 854–874,
2011.
[7] M. W. Achtelik, S. Lynen, S. Weiss, L. Kneip, M. Chli, and
R. Siegwart, “Visual-inertial SLAM for a small helicopter in large
outdoor environments,” 2012 IEEE/RSJ International Conference on
Intelligent Robots and Systems, pp. 2651–2652, Oct. 2012.
[8] G. Klein and D. Murray, “Parallel Tracking and Mapping for Small
AR Workspaces,” 2007 6th IEEE and ACM International Symposium
on Mixed and Augmented Reality, pp. 1–10, Nov. 2007.
[9] J. Engel, T. Sch¨ops, and D. Cremers, “LSD-SLAM: Large-Scale Direct
Monocular SLAM,” Computer VisionECCV 2014, pp. 1–16, 2014.
[10] C. Forster, M. Pizzoli, and D. Scaramuzza, “SVO: Fast Semi-Direct
Monocular Visual Odometry,” Proc. IEEE Intl. Conf. on Robotics and
Automation, 2014.
[11] M. Pizzoli, C. Forster, and D. Scaramuzza, “REMODE : Probabilistic
, Monocular Dense Reconstruction in Real Time,” Proc. IEEE
International Conference on Robotics and Automation (ICRA), 2014.
[12] G. Vogiatzis and C. Hern´andez, “Video-based, real-time multi-view
stereo,” Image and Vision Computing, vol. 29, no. 7, pp. 434–441, June
2011.
[13] R. A. Newcombe, S. J. Lovegrove, and A. J. Davison, “DTAM: Dense
tracking and mapping in real-time,” 2011 International Conference on
Computer Vision, pp. 2320–2327, Nov. 2011.
[14] C. Roussillon, A. Gonzalez, and J. Sol`a, “RT-SLAM: a generic and
real-time visual SLAM implementation,” Computer Vision Systems,
2011.
[15] J. Engel and D. Cremers, “Semi-Dense Visual Odometry for a
Monocular Camera,” IEEE International Conference on Computer
Vision (ICCV), 2013.
[16] D. Honegger and L. Meier, “An open source and open hardware
embedded metric optical flow CMOS camera for indoor and outdoor
applications,” Intl. Conf. Robotics and Automation (ICRA 2013), 2013.
[17] R. Mebarki and V. Lippiello, “Image moments-based velocity estimation
of UAVs in GPS denied environments,” in 2014 IEEE International
Symposium on Safety, Security, and Rescue Robotics (2014), 2014, pp.
1–6.
[18] T. Lee, M. Leok, and N. McClamroch, “Control of complex maneuvers
for a quadrotor UAV using geometric methods on SE (3),” arXiv
preprint arXiv:1003.2005, no. i, p. 8, Mar. 2010.
[19] F. Varesano, “FreeIMU: An Open Hardware Framework for Orientation
and Motion Sensing,” arXiv preprint, 2013.
[1] A. Bachrach and S. Prentice, “RANGE Robust autonomous navigation
in GPS denied environments,” Journal of Field Robotics, 2011.
[2] A. Bry, “State estimation for aggressive flight in GPS-denied
environments using onboard sensing,” International Conference on
Robotics and Automation, no. Icra, pp. 1–8, May 2012.
[3] J. Engel, J. Sturm, and D. Cremers, “Camera-based navigation of a
low-cost quadrocopter,” Intelligent Robots and Systems, pp. 2815–2821,
Oct. 2012.
[4] S. Shen, Y. Mulgaonkar, N. Michael, and V. Kumar, “Vision-Based
State Estimation and Trajectory Control Towards High-Speed Flight
with a Quadrotor.” Robotics: Science and Systems, 2013.
[5] E. Jones and S. Soatto, “Visual-inertial navigation, mapping and
localization: A scalable real-time causal approach,” The International
Journal of Robotics, pp. 1–38, 2011.
[6] S. Weiss, D. Scaramuzza, and R. Siegwart, “Monocular SLAM
based navigation for autonomous micro helicopters in GPS-denied
environments,” Journal of Field Robotics, vol. 28, no. 6, pp. 854–874,
2011.
[7] M. W. Achtelik, S. Lynen, S. Weiss, L. Kneip, M. Chli, and
R. Siegwart, “Visual-inertial SLAM for a small helicopter in large
outdoor environments,” 2012 IEEE/RSJ International Conference on
Intelligent Robots and Systems, pp. 2651–2652, Oct. 2012.
[8] G. Klein and D. Murray, “Parallel Tracking and Mapping for Small
AR Workspaces,” 2007 6th IEEE and ACM International Symposium
on Mixed and Augmented Reality, pp. 1–10, Nov. 2007.
[9] J. Engel, T. Sch¨ops, and D. Cremers, “LSD-SLAM: Large-Scale Direct
Monocular SLAM,” Computer VisionECCV 2014, pp. 1–16, 2014.
[10] C. Forster, M. Pizzoli, and D. Scaramuzza, “SVO: Fast Semi-Direct
Monocular Visual Odometry,” Proc. IEEE Intl. Conf. on Robotics and
Automation, 2014.
[11] M. Pizzoli, C. Forster, and D. Scaramuzza, “REMODE : Probabilistic
, Monocular Dense Reconstruction in Real Time,” Proc. IEEE
International Conference on Robotics and Automation (ICRA), 2014.
[12] G. Vogiatzis and C. Hern´andez, “Video-based, real-time multi-view
stereo,” Image and Vision Computing, vol. 29, no. 7, pp. 434–441, June
2011.
[13] R. A. Newcombe, S. J. Lovegrove, and A. J. Davison, “DTAM: Dense
tracking and mapping in real-time,” 2011 International Conference on
Computer Vision, pp. 2320–2327, Nov. 2011.
[14] C. Roussillon, A. Gonzalez, and J. Sol`a, “RT-SLAM: a generic and
real-time visual SLAM implementation,” Computer Vision Systems,
2011.
[15] J. Engel and D. Cremers, “Semi-Dense Visual Odometry for a
Monocular Camera,” IEEE International Conference on Computer
Vision (ICCV), 2013.
[16] D. Honegger and L. Meier, “An open source and open hardware
embedded metric optical flow CMOS camera for indoor and outdoor
applications,” Intl. Conf. Robotics and Automation (ICRA 2013), 2013.
[17] R. Mebarki and V. Lippiello, “Image moments-based velocity estimation
of UAVs in GPS denied environments,” in 2014 IEEE International
Symposium on Safety, Security, and Rescue Robotics (2014), 2014, pp.
1–6.
[18] T. Lee, M. Leok, and N. McClamroch, “Control of complex maneuvers
for a quadrotor UAV using geometric methods on SE (3),” arXiv
preprint arXiv:1003.2005, no. i, p. 8, Mar. 2010.
[19] F. Varesano, “FreeIMU: An Open Hardware Framework for Orientation
and Motion Sensing,” arXiv preprint, 2013.
@article{"International Journal of Mechanical, Industrial and Aerospace Sciences:71325", author = "Chang Liu and John Nash and Stephen D. Prior", title = "A Low-Cost Vision-Based Unmanned Aerial System for Extremely Low-Light GPS-Denied Navigation and Thermal Imaging", abstract = "This paper presents the design and implementation
details of a complete unmanned aerial system (UAS) based
on commercial-off-the-shelf (COTS) components, focusing on
safety, security, search and rescue scenarios in GPS-denied
environments. In particular, The aerial platform is capable
of semi-autonomously navigating through extremely low-light,
GPS-denied indoor environments based on onboard sensors only,
including a downward-facing optical flow camera. Besides, an
additional low-cost payload camera system is developed to stream
both infra-red video and visible light video to a ground station in
real-time, for the purpose of detecting sign of life and hidden humans.
The total cost of the complete system is estimated to be $1150,
and the effectiveness of the system has been tested and validated
in practical scenarios.", keywords = "Unmanned aerial system, commercial-off-the-shelf,
extremely low-light, GPS-denied, optical flow, infrared video.", volume = "9", number = "10", pages = "1807-8", }
