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Design and Performance Validation of Integrated Navigation System Based on Geometric Range Measurements and GIS Map for Urban Aerial Navigation

  • Control Theory and Applications
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Abstract

This article proposes an efficient integrated navigation algorithm to secure reliable navigation solutions in urban flight environments where satellite navigation is not available. Also, this study investigates a new sensor deployment and filter configuration that can perform real-time navigation onboard small drones by reducing the complexity and computational burden of the conventional point cloud-based pose estimation. The proposed method first derives the geometric relationship between the ranging vector and the known three-dimensional map, where the number of range sensors and deployment structure is refined. Then, we designed the inertial navigation filter structure combining the derived measurement model and evaluated the estimation performance in a realistic urban flight environment. The validity of the proposed algorithm is verified through both error analysis using a simulator with a high-fidelity model and real navigation error analysis from onboard flight test adjacent to urban buildings. In conclusion, this paper presents a distinctive navigation method from the existing point cloud-based approaches and the performance of real-time three-dimensional navigation with a position error of about 1–2 m in satellite unavailability environment.

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References

  1. R. Mur-Artal and J. D. Tardés, “ORB-SLAM2: An open-source SLAM system for monocular, stereo, and RGB-D cameras,” IEEE Transactions on Robotics, vol. 33, no. 5, pp. 1255–1262, 2017.

    Article  Google Scholar 

  2. S. Oishi, Y. Inoue, J. Miura, and S. Tanaka, “SeqSLAM++: View-based robot localization and navigation,” Robotics and Autonomous Systems, vol. 112, pp. 13–21, 2019.

    Article  Google Scholar 

  3. T. Ózaslan, G. Loianno, J. Keller, C. J. Taylor, V. Kumar, J. M. Wozencraft, and T. Hood, “Autonomous navigation and mapping for inspection of penstocks and tunnels with MAVs,” IEEE Robotics and Automation Letters, vol. 2, no. 3, pp. 1740–1747, 2017.

    Article  Google Scholar 

  4. G. Jiang, L. Yin, G. Liu, W. Xi, and Y. Ou, “FFT-based scan-matching for SLAM applications with low-cost laser range finders,” Applied Sciences, vol. 9, no. 1, pp. 41–58, 2019.

    Article  Google Scholar 

  5. L. T. Hsu, “Analysis and modeling GPS NLOS effect in highly urbanized area,” GPS Solutions, vol. 22, no. 7, 2018.

  6. Z. Zhang, B. Li, Y. Gao, and Y. Shen, “Real-time carrier phase multipath detection based on dual-frequency C/N0 data,” GPS Solutions, vol. 23, no. 7, 2019.

  7. S. Zahran, M. M. Mostafa, A. Masiero, A. M. Moussa, A. Vettore, and N. El-Sheimy, “Micro-radar and UWB aided UAV navigation in GNSS denied environment,” International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences, vol. 42, no. 1, pp. 469–476, 2018.

    Article  Google Scholar 

  8. S. Zahran, A. Moussa, and N. El-Sheimy, “Enhanced drone navigation in GNSS denied environment using VDM and hall effect sensor,” ISPRS International Journal of Geo-Information, vol. 8, no. 4, pp. 169–186, 2019.

    Article  Google Scholar 

  9. M. M. Atia and S. L. Waslander, “Map-aided adaptive GNSS/IMU sensor fusion scheme for robust urban navigation,” Measurement, vol. 131, pp. 615–627, 2019.

    Article  Google Scholar 

  10. A. P. Shetty, GPS-LiDAR sensor fusion aided by 3D city models for UAVs, M.S. Thesis, University of Illinois at Urbana-Champaign, 2017.

  11. D. Chen and G. X. Gao, “Probabilistic graphical fusion of LiDAR, GPS, and 3D building maps for urban UAV navigation,” Navigation, vol. 66, no. 1, pp. 151–168, 2019.

    Article  Google Scholar 

  12. Y. Choe, C. G. Park, and J. W. Song, “Importance sampling Kalman filter for urban canyon navigation,” Proc. of IEEE/ION Position Location and Navigation Symposium, pp.1264–1269, 2018.

  13. S. Rajeev, Q. Wan, K. Yau, K. Panetta, and S. S. Agaian, “Augmented reality-based vision-aid indoor navigation system in GPS denied environment,” Proc. of Mobile Multimedia/Image Processing, Security, and Applications, vol. 10993, pp. 109930–109939, 2019.

    Google Scholar 

  14. M. M. U. Chowdhury, F. Erden, and I. Guvenc, “RSS-based Q-learning for indoor UAV navigation,” arXiv preprint arXiv:1905.13406, 2019.[Online] https://arxiv.org/abs/1905.13406

  15. J. Tang, Y. Chen, X. Niu, L. Wang, L. Chen, J. Liu, C. Shi, and J. Hyyppa, “LiDAR scan matching aided inertial navigation system in GNSS-denied environments,” Sensors, vol. 15, no. 7, pp. 16710–16728, 2015.

    Article  Google Scholar 

  16. G. A. Kumar, A. K. Patil, R. Patil, S. S. Park, and Y. H. Chai, “A LiDAR and IMU integrated indoor navigation system for UAVs and its application in real-time pipeline classification,” Sensors, vol. 17, no. 6, pp. 1268–1291, 2017.

    Article  Google Scholar 

  17. H. Ye, Y. Chen, and M. Liu, “Tightly coupled 3D LiDAR inertial odometry and mapping,” Proc. of International Conference on Robotics and Automation, pp. 3144–3150, 2019.

  18. W. Yu and F. Amigoni, “Standard for robot map data representation for navigation,” Proc. of IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3–4, 2014.

  19. H. Kawasaki, T. Yatabe, K. Ikeuchi, and M. Sakauchi, “Automatic modeling of a 3D city map from real-world video,” Proc. of the 7th ACM International Conference on Multimedia (Part 1), pp. 11–18, 1999.

  20. J. Jeong, Y. Cho, Y. S. Shin, H. Roh, and A. Kim, “Complex urban lidar data set,” Proc. of IEEE International Conference on Robotics and Automation, pp. 6344–6351, 2018.

  21. B. Lee, G. Park, K. Ryu, Y. J. Lee, and S. Sung, “Design of integrated navigation system using IMU and multiple ranges from in-flight rotating hexacopter system,” Proc. of IEEE/ION Position Location and Navigation Symposium, pp. 673–679, 2018.

  22. B. Lee, G. Park, K. Ryu, and S. Sung, “Performance improvement of INS/multi range integrated navigation system under real flight environment,” Proc. of the 7th Asian/Australian Rotorcraft Forum, 2018.

  23. Y. Y. Chen, Z. Z. Wang, Y. Zhang, C. L. Liu, and Q. Wang, “A geometric extension design for spherical formation tracking control of second-order agents in unknown spatiotemporal flow fields,” Nonlinear Dynamics, vol. 88, no. 2, pp. 1173–1186, 2017.

    Article  Google Scholar 

  24. Y. Y. Chen and Y. P. Tian, “A curve extension design for coordinated path following control of unicycles along given convex loops,” International Journal of Control, vol. 84, no. 10, pp. 1729–1745, 2011.

    Article  MathSciNet  Google Scholar 

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Correspondence to Sangkyung Sung.

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Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Recommended by Associate Editor Aldo Jonathan Munoz-Vazquez under the direction of Editor Chan Gook Park. This research was supported by Konkuk University’s research support program for its faculty on sabbatical leave in 2019, the National Research Foundation of Korea (2015M3C1B1034536, 2019R1A2B5B01069412) and the Information Technology Research Center support program(IITP-2020-2018-0-01423).

Gwangsoo Park received his B.S. degree in aerospace engineering from Konkuk University, Seoul, Korea, in 2014. He is currently pursuing a Ph.D. degree at the Department of Aerospace Information Engineering, Konkuk University. His research interests include sensor fusion and integrated navigation system.

Byungjin Lee received his Ph.D. degree from the Department of Aerospace Information Engineering, Konkuk University in 2017. Now, he is with the Defense Agency for Technology and Quality, Korea. His research interests include the development of navigation and control system for unmanned vehicles.

Dong Gyun Kim received his B.S. and M.S. degrees in aerospace engineering from Konkuk University in 2014 and 2016, respectively. He is currently pursuing a Ph.D. degree at the Department of Aerospace Information Engineering, Konkuk University. His research interests include robust control theory, unmanned intelligent system and path planning.

Young Jae Lee is a Professor at the Department of Aerospace Information Engineering, Konkuk University. His research interests include integrity monitoring of GNSS signal, GBAS, RTK, attitude determination, orbit determination, and integrated navigation-related engineering problems.

Sangkyung Sung is a Professor at the Department of Aerospace Information Engineering, Konkuk University. His research interests include inertial sensors, integrated and seamless navigation, and application to mechatronics and unmanned intelligent systems.

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Park, G., Lee, B., Kim, D.G. et al. Design and Performance Validation of Integrated Navigation System Based on Geometric Range Measurements and GIS Map for Urban Aerial Navigation. Int. J. Control Autom. Syst. 18, 2509–2521 (2020). https://doi.org/10.1007/s12555-019-1059-4

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