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Vision-Based Localization for Multi-rotor Aerial Vehicle in Outdoor Scenarios

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Modelling and Simulation for Autonomous Systems (MESAS 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12619))

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Abstract

In this paper, we report on the experimental evaluation of the embedded visual localization system, the Intel RealSense T265, deployed on a multi-rotor unmanned aerial vehicle. The performed evaluation is targeted to examine the limits of the localization system and discover its weak points. The system has been deployed in outdoor rural scenarios at altitudes up to 20 m. The Absolute trajectory error measures the accuracy of the localization with the reference provided by the differential GPS with centimeter precision. Besides, the localization performance is compared to the state-of-the-art feature-based visual localization ORB-SLAM2 utilizing the Intel RealSense D435 depth camera. In both types of experimental scenarios, with the teleoperated and autonomous vehicle, the identified weak point of the system is a translation drift. However, taking into account all experimental trials, both examined localization systems provide competitive results.

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Notes

  1. 1.

    Available at https://github.com/IntelRealSense/realsense-ros.

References

  1. Agarwal, A., Crouse, J.R., Johnson, E.N.: Evaluation of a commercially available autonomous visual inertial odometry solution for indoor navigation. In: 2020 International Conference on Unmanned Aircraft Systems (ICUAS), pp. 372–381 (2020). https://doi.org/10.1109/ICUAS48674.2020.9213962

  2. Alapetite, A., Wang, Z., Hansen, J., Zaja̧czkowski, M., Patalan, M.: Comparison of three off-the-shelf visual odometry systems. Robotics 9, 56 (2020). https://doi.org/10.3390/robotics9030056

  3. Bayer, J., Faigl, J.: Localization fusion for aerial vehicles in partially GNSS denied environments. In: Mazal, J. (ed.) MESAS 2018. LNCS, vol. 11472, pp. 251–262. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-14984-0_20

    Chapter  Google Scholar 

  4. Bayer, J., Faigl, J.: On autonomous spatial exploration with small hexapod walking robot using tracking camera Intel RealSense T265. In: European Conference on Mobile Robots (ECMR), pp. 1–6 (2019). https://doi.org/10.1109/ECMR.2019.8870968

  5. Bayer, J., Faigl, J.: Handheld localization device for indoor environments. In: International Conference on Automation, Control and Robots (ICACR) (2020)

    Google Scholar 

  6. Collective of authors: vicon motion systems inc. https://www.vicon.com/. Accessed 1 Aug 2020

  7. Collective of authors: leica geosystems ag @ONLINE. http://leica-geosystems.com/products/total-stations. Accessed 3 Mar 2020

  8. Endres, F., Hess, J., Sturm, J., Cremers, D., Burgard, W.: 3-D mapping with an RGB-D camera. IEEE Trans. Robot. 30(1), 177–187 (2014). https://doi.org/10.1109/TRO.2013.2279412

    Article  Google Scholar 

  9. Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. 40(3), 611–625 (2016). https://doi.org/10.1109/TPAMI.2017.2658577

  10. Forster, C., Pizzoli, M., Scaramuzza, D.: SVO: fast semi-direct monocular visual odometry. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 15–22 (2014). https://doi.org/10.1109/ICRA.2014.6906584

  11. Fuentes-Pacheco, J., Ruiz-Ascencio, J., Rendón-Mancha, J.M.: Visual simultaneous localization and mapping: a survey. Artif. Intell. Rev. 43(1), 55–81 (2015). https://doi.org/10.1007/s10462-012-9365-8

    Article  Google Scholar 

  12. Gauglitz, S., Sweeney, C., Ventura, J., Turk, M., Höllerer, T.: Live tracking and mapping from both general and rotation-only camera motion. In: IEEE International Symposium on Mixed and Augmented Reality (ISMAR), pp. 13–22 (2012). https://doi.org/10.1109/ISMAR.2012.6402532

  13. Hofmann-Wellenhof, B., Lichtenegger, H., Wasle, E.: GNSS - Global Navigation Satellite Systems: GPS, GLONASS, Galileo, and More. Springer, Vienna (2007). https://books.google.cz/books?id=Np7y43HU_m8C

  14. Intel RealSense depth camera D435. https://www.intelrealsense.com/depth-camera-d435/. Accessed 4 Aug 2020

  15. Intel RealSense tracking camera T265. https://www.intelrealsense.com/tracking-camera-t265/. Accessed 4 Aug 2020

  16. Kim, D., et al.: Vision aided dynamic exploration of unstructured terrain with a small-scale quadruped robot. In: 2020 IEEE International Conference on Robotics and Automation (ICRA), pp. 2464–2470 (2020). https://doi.org/10.1109/ICRA40945.2020.9196777

  17. Lightbody, P., Krajník, T., Hanheide, M.: A versatile high-performance visual fiducial marker detection system with scalable identity encoding. In: Proceedings of the Symposium on Applied Computing, pp. 276–282. ACM (2017)

    Google Scholar 

  18. Mahmoud, A., Atia, M.M.: Hybrid IMU-aided approach for optimized visual odometry. In: 2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP), pp. 1–5 (2019). https://doi.org/10.1109/GlobalSIP45357.2019.8969460

  19. Menze, M., Geiger, A.: Object scene flow for autonomous vehicles. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3061–3070 (2015). https://doi.org/10.1109/CVPR.2015.7298925

  20. Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: an open-source SLAM system for monocular, stereo, and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255–1262 (2017). https://doi.org/10.1109/TRO.2017.2705103

    Article  Google Scholar 

  21. Mur-Artal, R., Montiel, J.M.M., Tardós, J.D.: ORB-SLAM: a versatile and accurate monocular SLAM system. IEEE Trans. Robot. 31(5), 1147–1163 (2015). https://doi.org/10.1109/TRO.2015.2463671

    Article  Google Scholar 

  22. Opromolla, R., Fasano, G., Rufino, G., Grassi, M., Savvaris, A.: LIDAR-inertial integration for UAV localization and mapping in complex environments. In: International Conference on Unmanned Aircraft Systems (ICUAS), pp. 444–457 (2016). https://doi.org/10.1109/ICUAS.2016.7502580

  23. Ouerghi, S., Ragot, N., Boutteau, R., Savatier, X.: Comparative study of a commercial tracking camera and orb-slam2 for person localization. In: Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, pp. 357–364. INSTICC, SciTePress (2020). https://doi.org/10.5220/0008980703570364

  24. Pire, T., Fischer, T., Castro, G., Cristóforis, P.D., Civera, J., Berlies, J.J.: S-PTAM: stereo parallel tracking and mapping. Robot. Auton. Syst. 93, 27–42 (2017). http://www.sciencedirect.com/science/article/pii/S0921889015302955

  25. Quigley, M., et al.: ROS: an open-source robot operating system. In: ICRA Workshop on Open Source Software (2009)

    Google Scholar 

  26. Smith, M., Baldwin, I., Churchill, W., Paul, R., Newman, P.: The new college vision and laser data set. Int. J. Robot. Res. 28(5), 595–599 (2009). https://doi.org/10.1177/0278364909103911

    Article  Google Scholar 

  27. ZED Mini. https://www.stereolabs.com/zed-mini/. Accessed 4 Aug 2020

  28. Sturm, J., Engelhard, N., Endres, F., Burgard, W., Cremers, D.: A benchmark for the evaluation of RGB-D SLAM systems. In: IEEE International Conference on Intelligent Robots and Systems (IROS), pp. 573–580 (2012). https://doi.org/10.1109/IROS.2012.6385773

  29. Usenko, V., Engel, J., Stückler, J., Cremers, D.: Direct visual-inertial odometry with stereo cameras. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 1885–1892 (2016). https://doi.org/10.1109/ICRA.2016.7487335

  30. Zhou, Y., Kneip, L., Li, H.: Semi-dense visual odometry for RGB-D cameras using approximate nearest neighbour fields. In: 2017 IEEE International Conference on Robotics and Automation (ICRA), pp. 6261–6268 (2017). https://doi.org/10.1109/ICRA.2017.7989742

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Acknowledgement

The presented work has been supported by the Technology Agency of the Czech Republic (TAČR) under research Project No. TH03010362 and under the OP VVV funded project CZ.02.1.01/0.0/0.0/16_019/0000765 “Research Center for Informatics”. The support under grant No. SGS19/176/OHK3/3T/13 to Jan Bayer is also gratefully acknowledged.

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Authors and Affiliations

  1. Faculty of Electrical Engineering, Czech Technical University in Prague, Technicka 2, 166 27, Prague, Czech Republic

    Jan Bayer & Jan Faigl

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  1. Jan Bayer
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  2. Jan Faigl
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Correspondence to Jan Bayer .

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Editors and Affiliations

  1. NATO M&S COE, Rome, Italy

    Jan Mazal

  2. University of Palermo, Palermo, Italy

    Adriano Fagiolini

  3. Brno University of Technology, Brno, Czech Republic

    Petr Vasik

  4. NATO M&S COE, Rome, Italy

    Michele Turi

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Bayer, J., Faigl, J. (2021). Vision-Based Localization for Multi-rotor Aerial Vehicle in Outdoor Scenarios. In: Mazal, J., Fagiolini, A., Vasik, P., Turi, M. (eds) Modelling and Simulation for Autonomous Systems. MESAS 2020. Lecture Notes in Computer Science(), vol 12619. Springer, Cham. https://doi.org/10.1007/978-3-030-70740-8_14

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  • DOI: https://doi.org/10.1007/978-3-030-70740-8_14

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  • Online ISBN: 978-3-030-70740-8

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