Block Matching Based Obstacle Avoidance for Unmanned Aerial Vehicle

  • Adomas Ivanovas
  • Armantas Ostreika
  • Rytis Maskeliūnas
  • Robertas Damaševičius
  • Dawid Połap
  • Marcin WoźniakEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10841)


Unmanned aerial vehicles (UAVs) are becoming very popular now. They have a variety of applications: search and rescue missions, crop inspection, 3D mapping, surveillance and military applications. However, many of the lower-end UAV do not have obstacle avoidance systems installed, which can lead to broken equipment or people may get injured. In this paper, we describe the design of low-cost UAV with computer vision based obstacle avoidance system. We used Block Match (BM) and Semi Global Block Match (SGBM) algorithms for detection of obstacles in stereo images. We constructed custom UAV platform, and demonstrated the effectiveness of UAV with an obstacle avoidance system in real-world field testing conditions.


Stereo vision Obstacle avoidance Unmanned aerial vehicle Drone 



The Authors would like to acknowledge contribution to this research from the “Diamond Grant 2016” No. 0080/DIA/2016/45 funded by the Polish Ministry of Science and Higher Education.


  1. 1.
    Austin, R.: Unmanned Aircraft Systems: UAVS Design, Development and Deployment. Wiley, Hoboken (2010)CrossRefGoogle Scholar
  2. 2.
    Naidoo, Y., Stopforth, R., Bright, G.: Development of an UAV for search & rescue applications. In: AFRICON 2011, Livingstone, pp. 1–6 (2011).
  3. 3.
    Zhang, C., Walters, D., Kovacs, J.M.: Applications of low altitude remote sensing in agriculture upon farmers’ requests-a case study in northeastern Ontario, Canada. PLoS One 9(11), e112894 (2014). Scholar
  4. 4.
    Jones, G.P., Pearlstine, L.G., Percival, H.F.: An assessment of small unmanned aerial vehicles for wildlife research. Wildl. Soc. Bull. 34(3), 750–758 (2006)CrossRefGoogle Scholar
  5. 5.
    Cermak, P., Martinu, J.: Component based design of mini UAV. In: International Conference on Military Technologies, ICMT 2015, pp. 1–5 (2015).
  6. 6.
    Ashraf, A., Majd, A., Troubitsyna, E.: Towards a realtime, collision-free motion coordination and navigation system for a UAV fleet. In: Rysavy, O., Vranić, V., Papadopoulos, G.A. (eds.) Proceedings of the Fifth European Conference on the Engineering of Computer-Based Systems (ECBS 2017), Article no. 11, p. 9. ACM, New York (2017).
  7. 7.
    Goppert, J.M., Wagoner, A.R., Schrader, D.K., Ghose, S., Kim, Y., Park, S., Gomez, M., Matson, E.T., Hopmeier, M.J.: Realization of an autonomous, air-to-air counter unmanned aerial system (CUAS). In: First IEEE International Conference on Robotic Computing (IRC), Taichung, pp. 235–240 (2017)Google Scholar
  8. 8.
    Wagoner, A.R., Schrader, D.K., Matson, E.T.: Survey on detection and tracking of UAVs using computer vision. In: First IEEE International Conference on Robotic Computing (IRC), Taichung, pp. 320–325 (2017).
  9. 9.
    Budiyanto, A., Cahyadi, A., Adji, T.B., Wahyunggoro, O.: UAV obstacle avoidance using potential field under dynamic environment. In: International Conference on Control, Electronics, Renewable Energy and Communications, ICCEREC 2015, pp. 187–192 (2015).
  10. 10.
    Borenstein, J., Everett, H.R., Feng, L., Wehe, D.: Mobile robot positioning: sensors and techniques. J. Robot. Syst. 14(4), 231–249 (1997)CrossRefGoogle Scholar
  11. 11.
    Jian, L., Xiao-min, L.: Vision-based navigation and obstacle detection for UAV. In: 2011 International Conference on Electronics, Communications and Control, pp. 1771–1774 (2011)Google Scholar
  12. 12.
    Kwag, Y.K., Choi, M.S., Jung, C.H., Hwang, K.Y.: Collision avoidance radar for UAV. In: 2006 CIE International Conference on Radar, pp. 1–4 (2006).
  13. 13.
    Luo, D., Wang, F., Wang, B., Chen, B.M.: Implementation of obstacle avoidance technique for indoor coaxial rotorcraft with scanning laser range finder. In: Proceedings of the 31st Chinese Control Conference, Hefei, pp. 5135–5140 (2012)Google Scholar
  14. 14.
    Mader, D., Blaskow, R., Westfeld, P., Maas, H.: UAV-based acquisition of 3D point cloud - a comparison of a low-cost laser scanner and SFM-tools. Int. Arch. Photogrammetry Remote Sens. Spatial Inf. Sci. - ISPRS Arch. 40(3W3), 335–341 (2015).
  15. 15.
    Chakravarty, P., Kelchtermans, K., Roussel, T., Wellens, S., Tuytelaars, T., Van Eycken, L.: CNN-based single image obstacle avoidance on a quadrotor. In: IEEE International Conference on Robotics and Automation, pp. 6369–6374 (2017).
  16. 16.
    Je, C., Park, H.-M.: Optimized hierarchical block matching for fast and accurate image registration. Sign. Process.: Image Commun. 28, 779–791 (2013)Google Scholar
  17. 17.
    Yang, J., Wang, H., Ding, Z., Lv, Z., Wei, W., Song, H.: Local stereo matching based on support weight with motion flow for dynamic scene. IEEE Access 4, 4840–4847 (2016). Scholar
  18. 18.
    Hirschmüller, H.: Stereo processing by semi-global matching and mutual information. IEEE Trans. Pattern Anal. Mach. Intell. 30(2), 328–341 (2008)CrossRefGoogle Scholar
  19. 19.
    Esmaeili, A., et al.: The impact of diversity on performance of holonic multi-agent systems. Eng. Appl. Artif. Intell. 55, 186–201 (2016)CrossRefGoogle Scholar
  20. 20.
    Min, B.-C., et al.: A directional antenna based leader–follower relay system for end-to-end robot communications. Robot. Auton. Syst. 101, 57–73 (2018)CrossRefGoogle Scholar
  21. 21.
    Łągiewka, M., Korytkowski, M., Scherer, R.: Distributed image retrieval with color and keypoint features. In: 2017 IEEE International Conference on INnovations in Intelligent SysTems and Applications (INISTA). IEEE (2017)Google Scholar
  22. 22.
    Najgebauer, P., Rutkowski, L., Scherer, R.: Novel method for joining missing line fragments for medical image analysis. In: 2017 22nd International Conference on Methods and Models in Automation and Robotics (MMAR). IEEE (2017)Google Scholar
  23. 23.
    Esmaeili, A., et al.: A socially-based distributed self-organizing algorithm for holonic multi-agent systems: case study in a task environment. Cogn. Syst. Res. 43, 21–44 (2017)CrossRefGoogle Scholar
  24. 24.
    Gabryel, M., Damaševičius, R.: The image classification with different types of image features. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2017. LNCS (LNAI), vol. 10245, pp. 497–506. Springer, Cham (2017). Scholar
  25. 25.
    Grycuk, R., et al.: Content-based image retrieval optimization by differential evolution. In: 2016 IEEE Congress on Evolutionary Computation (CEC). IEEE (2016)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Adomas Ivanovas
    • 1
  • Armantas Ostreika
    • 1
  • Rytis Maskeliūnas
    • 1
  • Robertas Damaševičius
    • 1
  • Dawid Połap
    • 2
  • Marcin Woźniak
    • 2
    Email author
  1. 1.Department of Multimedia EngineeringKaunas University of TechnologyKaunasLithuania
  2. 2.Institute of Mathematics, Faculty of Applied MathematicsSilesian University of TechnologyGliwicePoland

Personalised recommendations