A Version of Libviso2 for Central Dioptric Omnidirectional Cameras with a Laser-Based Scale Calculation

  • André AguiarEmail author
  • Filipe Santos
  • Luís Santos
  • Armando Sousa
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1092)


Monocular Visual Odometry techniques represent a challenging and appealing research area in robotics navigation field. The use of a single camera to track robot motion is a hardware-cheap solution. In this context, there are few Visual Odometry methods on the literature that estimate robot pose accurately using a single camera without any other source of information. The use of omnidirectional cameras in this field is still not consensual. Many works show that for outdoor environments the use of them does represent an improvement compared with the use of conventional perspective cameras. Besides that, in this work we propose an open-source monocular omnidirectional version of the state-of-the-art method Libviso2 that outperforms the original one even in outdoor scenes. This approach is suitable for central dioptric omnidirectional cameras and takes advantage of their wider field of view to calculate the robot motion with a really positive performance on the context of monocular Visual Odometry. We also propose a novel approach to calculate the scale factor that uses matches between laser measures and 3-D triangulated feature points to do so. The novelty of this work consists in the association of the laser ranges with the features on the omnidirectional image. Results were generate using three open-source datasets built in-house showing that our unified system largely outperforms the original monocular version of Libviso2.



This work is co-financed by the European Regional Development Fund (ERDF) through the Interreg V-A Espanha-Portugal Programme (POCTEP) 2014–2020 within project 0095_BIOTECFOR_1_P. This work also was co-financed by the ERDF European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation - COMPETE 2020 under the PORTUGAL 2020 Partnership Agreement, and through the Portuguese National Innovation Agency (ANI) as a part of project “ROMOVI: POCI-01-0247-FEDER-017945” The opinions included in this paper shall be the sole responsibility of their authors. The European Commission and the Authorities of the Programme aren’t responsible for the use of information contained therein.


  1. 1.
    Aguiar, A., Sousa, A., Santos, F., Oliveira, M.: Monocular visual odometry benchmarking and turn performance optimization. In: 19th IEEE International Conference on Autonomous Robot Systems and Competitions, April 2019Google Scholar
  2. 2.
    Caruso, D., Engel, J., Cremers, D.: Large-scale direct SLAM for omnidirectional cameras. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, September 2015Google Scholar
  3. 3.
    Geiger, A., Ziegler, J., Stiller, C.: StereoScan: Dense 3D reconstruction in real-time. In: IEEE Intelligent Vehicles Symposium (IV). IEEE, June 2011Google Scholar
  4. 4.
    Giubilato, R., Chiodini, S., Pertile, M., Debei, S.: Scale correct monocular visual odometry using a lidar altimeter. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 3694–3700, October 2018Google Scholar
  5. 5.
    Gräter, J., Wilczynski, A., Lauer, M.: LIMO: lidar-monocular visual odometry. CoRR abs/1807.07524 (2018).
  6. 6.
    Kohlbrecher, S., von Stryk, O., Meyer, J., Klingauf, U.: A flexible and scalable slam system with full 3D motion estimation. In: IEEE International Symposium on Safety, Security, and Rescue Robotics, pp. 155–160, November 2011Google Scholar
  7. 7.
    Matsuki, H., von Stumberg, L., Usenko, V., Stueckler, J., Cremers, D.: Omnidirectional DSO: Direct sparse odometry with fisheye cameras. In: IEEE Robotics and Automation Letters (RA-L) & International Conference on Intelligent Robots and Systems (IROS) (2018)Google Scholar
  8. 8.
    Raju, V.K.T.P.: Fisheye camera calibration and applications. Master’s thesis, Arizona State University (2014)Google Scholar
  9. 9.
    Reis, R., Mendes, J., Neves dos Santos, F., Morais, R., Ferraz, N., Santos, L., Sousa, A.: Redundant robot localization system based in wireless sensor network. In: IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC), pp. 154–159, April 2018Google Scholar
  10. 10.
    Rituerto, A., Puig, L., Guerrero, J.J.: Comparison of omnidirectional and conventional monocular systems for visual SLAM Google Scholar
  11. 11.
    Santos, L., Ferraz, N., Neves dos Santos, F., Mendes, J., Morais, R., Costa, P., Reis, R.: Path planning aware of soil compaction for steep slope vineyards. In: IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC), pp. 250–255, April 2018Google Scholar
  12. 12.
    Scaramuzza, D., Martinelli, A., Siegwart, R.: A flexible technique for accurate omnidirectional camera calibration and structure from motion. In: Fourth IEEE International Conference on Computer Vision Systems (ICVS 2006). IEEE (2006)Google Scholar
  13. 13.
    Scaramuzza, D., Fraundorfer, F.: Visual odometry [tutorial]. IEEE Robot. Autom. Mag. 18(4), 80–92 (2011)CrossRefGoogle Scholar
  14. 14.
    Scaramuzza, D., Martinelli, A., Siegwart, R.: A toolbox for easily calibrating omnidirectional cameras. In: IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, October 2006Google Scholar
  15. 15.
    Tardif, J.P., Pavlidis, Y., Daniilidis, K.: Monocular visual odometry in urban environments using an omnidirectional camera. In: IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, September 2008Google Scholar
  16. 16.
    Wu, K., Di, K., Sun, X., Wan, W., Liu, Z.: Enhanced monocular visual odometry integrated with laser distance meter for astronaut navigation. Sensors 14, 4981–5003 (2014)CrossRefGoogle Scholar
  17. 17.
    Zhang, Z., Rebecq, H., Forster, C., Scaramuzza, D.: Benefit of large field-of-view cameras for visual odometry. In: IEEE International Conference on Robotics and Automation (ICRA). IEEE, May 2016Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • André Aguiar
    • 1
    Email author
  • Filipe Santos
    • 1
  • Luís Santos
    • 1
  • Armando Sousa
    • 2
  1. 1.INESC TEC - INESC Technology and SciencePortoPortugal
  2. 2.Faculty of Engineering of University of PortoPortoPortugal

Personalised recommendations