Integrated Navigation Design Using a Gimbaled Vision/LiDAR System with an Approximate Ground Description Model

Yun, Sukchang;Lee, Young Jae;Kim, Chang Joo;Sung, Sangkyung

  • Received : 2013.08.16
  • Accepted : 2013.12.26
  • Published : 2013.12.30


This paper presents a vision/LiDAR integrated navigation system that provides accurate relative navigation performance on a general ground surface, in GNSS-denied environments. The considered ground surface during flight is approximated as a piecewise continuous model, with flat and slope surface profiles. In its implementation, the presented system consists of a strapdown IMU, and an aided sensor block, consisting of a vision sensor and a LiDAR on a stabilized gimbal platform. Thus, two-dimensional optical flow vectors from the vision sensor, and range information from LiDAR to ground are used to overcome the performance limit of the tactical grade inertial navigation solution without GNSS signal. In filter realization, the INS error model is employed, with measurement vectors containing two-dimensional velocity errors, and one differenced altitude in the navigation frame. In computing the altitude difference, the ground slope angle is estimated in a novel way, through two bisectional LiDAR signals, with a practical assumption representing a general ground profile. Finally, the overall integrated system is implemented, based on the extended Kalman filter framework, and the performance is demonstrated through a simulation study, with an aircraft flight trajectory scenario.


Vision/LiDAR;navigation;ground slope model;integrated system;extended Kalman filter


  1. C. Becker, J. Salas, K. Tokusei, and J. C. Latombe, "Reliable Navigation Using Landmarks", Proceedings of IEEE International Conference on Robotics and Automation, Nagoya, Aichi, 1995, Vol.1, pp. 401-406.
  2. C. S. Sharp, O. Shakernia and S.S. Sastry, "A Vision System for Landing an Unmanned Aerial Vehicle", Proceedings of IEEE International Conference on Robotics and Automation, 2001, Vol.2, pp. 1720-1727.
  3. L. S. Coelho and M.F.M. Campos, "Pose Estimation of Autonomous Dirigibles Using Artificial Landmarks", Proceedings of IEEE International Conference on Robotics and Automation, Campinas, Brazil, 1999, pp. 161-170.
  4. H. Durrant-Whyte and T. Bailey, "Simultaneous Localization and Mapping: Part I", IEEE Robotics & Automation Magazine, 2006, Vol.13, Issue2, pp. 99-110.
  5. T. Bailey and H. Durrant-Whyte, "Simultaneous Localization and Mapping (SLAM): Part II", IEEE Robotics & Automation Magazine, 2006, Vol.13, Issue3, pp. 108-117.
  6. J. Kim, and S. Sukkarieh, "Real-time Implementation of Airborne Inertial-SLAM", Robotics and Autonomous Systems, 2007, Vol.55, Issue1, pp. 62-71.
  7. S. Ahrens, D. Levine, G. Andrews and J.P. How, "Vision-based guidance and control of a hovering vehicle in unknown, GPS-denied environments", Proceedings of IEEE International Conference on Robotics and Automation, Kobe, Japan, 2009, pp. 2643-2648.
  8. M. K. Kaiser, N. R. Gans and W. E. Dixon, "Vision-Based Estimation for Guidance, Navigation, and Control of an Aerial Vehicle", IEEE Transactions on Aerospace and Electronic Systems, 2010, Vol.46, Issue3, pp. 1064-1077.
  9. S. Yun, B. Lee, Y. J. Lee and S. Sung "Real-Time Performance Test of an Vision-based Inertial SLAM", Proceedings of International Conference on Control, Automation and Systems, 2010, Gyeonggi-do, Korea, pp. 2423-2426.
  10. T. Cornall and G. Egan, "Optic flow methods applied to unmanned air vehicles", Academic Research Forum, Dept. Elect. And Computer Systems Engineering, Monash University, Feb, 2003.
  11. A. Giachetti, M. Campani and V. Torre, "The Use of Optical Flow for Road Navigation", IEEE Transactions Robotics and Automation, 1998, Vol. 14, Issue1, pp. 34-48.
  12. W. Ding, J. Wang, S. Han, A. Almagbile, M. A. Garratt, A. Lambert, and J. J. Wang, "Adding Optical Flow into the GPS/INS Integration for UAV navigation", International Global Navigation Satellite Society Symposium, 2009, Surfers Paradise, Qld, Australia.
  13. S. Zingg, D. Scaramuzza, S. Weiss, and R. Siegwart, "MAV Navigation through Indoor Corridors Using Optical Flow", Proceedings of IEEE International Conference on Robotics and Automation, Anchorage, AK, 2010, pp. 3361-3368.
  14. F. Kendoul, I. Fantoni, and K. Nonami, "Optic Flow-Based Vision System for Autonomous 3D Localization and Control of Small Aerial Vehicles", Robotics and Autonomous Systems, 2009, Vol.57, Issue 6-7, pp. 591-602.
  15. S. Kohlbrecher, J. Meyer, O. von Stryk, and U. Klingauf, "A Flexible and Scalable SLAM System with Full 3D Motion Estimation", IEEE International Symposium on Safety, Security, and Rescue Robotics, 2011, Kyoto, Japan, pp. 155-160.
  16. C. Premebida, O. Ludwig and U. Nunes, "LIDAR and Vision-Based Pedestrian Detection System", Journal of Field Robotics, 2009, Vol.26, No. 9, pp. 696-711.
  17. L. Huang and M. Barth, "Tightly-Coupled LIDAR and Computer Vision Integration for Vehicle Detection", IEEE Intelligent Vehicles Symposium, Xi'an, China, 2009, pp. 604-609.
  18. P. Moghadam, W. S. Wijesoma, and J. F. Dong, "Improving Path Planning and Mapping Based on Stereo Vision and Lidar", Proceedings of International Conference on Control, Automation, Robotics and Vision, Hanoi, Vietnam, 2008, pp. 384-389.
  19. W. Ding, Optimal Integration of GPS with Inertial Sensors: Modelling and Implementation, Ph.D. thesis, University of New South Wales, Sydney, 2008.
  20. Ribeiro, M., "Kalman and extended Kalman filters: concept, derivation and properties", Technical Report, Institute for Systems and Robotics-Instituto Superior Tecnico, Lisbon, 2004.

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