Accuracy of Autonomy Navigation of Unmanned Aircraft Systems through Imagery

Authors: Sidney A. Lima, Hermann J. H. Kux, Elcio H. Shiguemori

Abstract:

The Unmanned Aircraft Systems (UAS) usually navigate through the Global Navigation Satellite System (GNSS) associated with an Inertial Navigation System (INS). However, GNSS can have its accuracy degraded at any time or even turn off the signal of GNSS. In addition, there is the possibility of malicious interferences, known as jamming. Therefore, the image navigation system can solve the autonomy problem, because if the GNSS is disabled or degraded, the image navigation system would continue to provide coordinate information for the INS, allowing the autonomy of the system. This work aims to evaluate the accuracy of the positioning though photogrammetry concepts. The methodology uses orthophotos and Digital Surface Models (DSM) as a reference to represent the object space and photograph obtained during the flight to represent the image space. For the calculation of the coordinates of the perspective center and camera attitudes, it is necessary to know the coordinates of homologous points in the object space (orthophoto coordinates and DSM altitude) and image space (column and line of the photograph). So if it is possible to automatically identify in real time the homologous points the coordinates and attitudes can be calculated whit their respective accuracies. With the methodology applied in this work, it is possible to verify maximum errors in the order of 0.5 m in the positioning and 0.6º in the attitude of the camera, so the navigation through the image can reach values equal to or higher than the GNSS receivers without differential correction. Therefore, navigating through the image is a good alternative to enable autonomous navigation.

Keywords: Autonomy, navigation, security, photogrammetry, remote sensing, spatial resection, UAS.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1316456

References:

[1] J. A. Gonçalves and R. Henriques, “UAV photogrammetry for topographic monitoring of coastal areas,” ISPRS J. Photogramm. Remote Sens., vol. 104, pp. 101–111, 2015.
[2] J. Roberts, D. Frousheger, B. Williams, D. Campbell, and R. Walker, “How the outback challenge was won,” IEEE Robot. Autom. Mag., vol. 23, no. 4, pp. 54–62, 2016.
[3] R. O. Andrade, “O voo do falcão.” FAPESP, São Paulo, p. v.211, 64-69, 2013.
[4] P. J. Zarco-Tejada, R. Diaz-Varela, V. Angileri, and P. Loudjani, “Tree height quantification using very high resolution imagery acquired from an unmanned aerial vehicle (UAV) and automatic 3D photo-reconstruction methods,” Eur. J. Agron., vol. 55, pp. 89–99, 2014.
[5] J. P. Dash, M. S. Watt, G. D. Pearse, M. Heaphy, and H. S. Dungey, “Assessing very high resolution UAV imagery for monitoring forest health during a simulated disease outbreak,” ISPRS J. Photogramm. Remote Sens., vol. 131, pp. 1–14, 2017.
[6] H. Sun, G. Song, Z. Wei, Y. Zhang, and S. Liu, “Bilateral teleoperation of an unmanned aerial vehicle for forest fire detection,” 2017 IEEE Int. Conf. Inf. Autom. ICIA 2017, no. July, pp. 586–591, 2017.
[7] M. R. James, S. Robson, S. D’Oleire-Oltmanns, and U. Niethammer, “Optimising UAV topographic surveys processed with structure-from-motion: Ground control quality, quantity and bundle adjustment,” Geomorphology, vol. 280, pp. 51–66, 2017.
[8] F. C. Nogueira and L. Roberto, “Accuracy analysis of orthomosaic and DSM produced from sensor aboard UAV,” XVIII Simpósio Bras. Sensoriamento Remoto -SBSR, vol. d, no. 2011, pp. 4880–4887, 2017.
[9] F. Agüera-Vega, F. Carvajal-Ramírez, and P. Martínez-Carricondo, “Assessment of photogrammetric mapping accuracy based on variation ground control points number using unmanned aerial vehicle,” Meas. J. Int. Meas. Confed., vol. 98, 2017.
[10] F. Agüera-Vega and F. Carvajal-Ramírez, “Accuracy of Digital Surface Models and Orthophotos Derived from Unmanned Aerial Vehicle Photogrammetry,” J. Surv., vol. 143, no. 2, pp. 1–10, 2016.
[11] J. R. G. Braga, H. F. de C. Velho, G. Conte, P. Doherty, and E. H. Shiguemori, “An Image Matching System for Autonomous UAV Navigation Based on Neural Network,” International Conference on Control, Automation, Robotics & Vision (ICARCV), Phuket, Thailand, p. 6, 2016.
[12] C. F. Lo et al., “The direct georeferencing application and performance analysis of UAV helicopter in GCP-free area,” ISPRS - Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci., vol. XL-1/W4, no. 1W4, pp. 151–157, Aug. 2015.
[13] S. A. Lima, L. Roberto, E. H. Shiguemori, H. J. H. Kux, and J. L. N. e S. Brito, “Determinação da posição e atitudes de VANT por fotogrametria,” XVIII Simpósio Bras. Sensoriamento Remoto - SBSR, no. 2008, pp. 5392–5399, 2017.
[14] G. Conte and P. Doherty, “a Visual Navigation System for UAS Based on Geo-Referenced Imagery,” ISPRS - Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci., vol. XXXVIII-1/, no. September, pp. 1–6, 2011.
[15] L. de A. Faria, C. A. de M. Silvestre, and M. A. F. Correia, “GPS-dependent systems: Vulnerabilities to electromagnetic attacks,” J. Aerosp. Technol. Manag., vol. 8, no. 4, pp. 423–430, 2016.
[16] P. F. F. Silva Filho, “Automatic Landmark Recognition in Aerial Images for the Autonomous Navigation System of Unmanned Aerial Vehicles,” Instituto Tecnológico de Aeronautica - ITA, São José dos Campos, 2016.
[17] P. R. Wolf, B. A. Dewitt, and B. E. Wilkinson, Elements of Photogrammetry with Applications in GIS. New York: Mc Graw Hill Education, 2014.
[18] J. C. McGlone and G. Y. G. Lee, Manual of Photogrammetry, Sixth. Bethesda, 2013.
[19] C. D. Ghilani, Adjustament Computations: Spatial Data Analysis. New Jersey: Wily, 2017.
[20] E. M. Mikhail, J. S. Bethel, and J. C. McGlone, Introduction to Modern Photogrammetry. Hobeken: Wiley, 2001.
[21] DECEA/ICA_100-40, Sistemas de Aeronaves Remotamente Pilotadas e o Acesso ao Espaço Aéreo Brasileiro - ICA 100-40. Brasil, 2016, p. 56.