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Unscented Particle Filters with Refinement Steps for UAV Pose Tracking

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Abstract

Particle Filters (PFs) have been successfully employed for monocular 3D model-based tracking of rigid objects. However, these filters depend on the computation of importance weighs that use sub-optimal approximations to the likelihood function. In this paper, we propose to enrich the filter with additional refinement steps to abridge its sub-optimality. We test the proposed approach in two different types of PFs: (i) an Unscented Particle Filter (UPF), and (ii) the recently proposed Unscented Bingham Filter (UBiF). These filters are applied to the outdoor tracking of a fixed-wing Unmanned Aerial Vehicle (UAV) autonomous landing in a Fast Patrol Boat (FPB), tested in a simulated environment with a real sky gradient filled with clouds. The use of the refinement steps significantly improves the overall accuracy of the method.

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References

  1. Abbeel, P., Coates, A., Ng, A.Y.: Autonomous helicopter aerobatics through apprenticeship learning. Int. J. Robot. Res. 29(13), 1608–1639 (2010)

    Article  Google Scholar 

  2. Allan, M., Ourselin, S., Thompson, S., Hawkes, D.J., Kelly, J., Stoyanov, D.: Toward detection and localization of instruments in minimally invasive surgery. IEEE Trans. Biomed. Eng. 60(4), 1050–1058 (2012)

    Article  Google Scholar 

  3. Artieda, J., Sebastian, J.M., Campoy, P., Correa, J.F., Mondragón, I.F., Martínez, C., Olivares, M.: Visual 3-d slam from uavs. J. Intell. Robot. Sys. 55(4-5), 299 (2009)

    Article  Google Scholar 

  4. Azinheira, J.R., Rives, P.: Image-based visual servoing for vanishing features and ground lines tracking: Application to a uav automatic landing. Int. J. Optomechatron. 2(3), 275–295 (2008)

    Article  Google Scholar 

  5. Bar-Shalom, Y., Li, X.R., Kirubarajan, T.: Estimation with applications to tracking and navigation: theory algorithms and software. Wiley, Hoboken (2004)

    Google Scholar 

  6. Bazin, J.C., Demonceaux, C., Vasseur, P., Kweon, I.: Rotation estimation and vanishing point extraction by omnidirectional vision in urban environment. Int. J. Robot. Res. 31(1), 63–81 (2012)

    Article  Google Scholar 

  7. Bingham, C.: An antipodally symmetric distribution on the sphere, Ann Stat, 1201–1225 (1974)

  8. Birsan, M.: Unscented particle filter for tracking a magnetic dipole target. In: OCEANS 2005. Proceedings of MTS/IEEE, pp 1656–1659. IEEE (2005)

  9. Challa, S.: Fundamentals of object tracking. Cambridge University Press, Cambridge (2011)

    Book  Google Scholar 

  10. Cheng, J., Grossman, M., McKercher, T.: Professional CUDA c programming. Wiley, Hoboken (2014)

    Google Scholar 

  11. Cheng, Y., Crassidis, J.: Particle filtering for sequential spacecraft attitude estimation. In: AIAA guidance, navigation, and control conference and exhibit, p 5337 (2004)

  12. Conway, A.W.: Quaternion treatment of the relativistic wave equation. Proc. Royal Soc. London Ser. A-Math. Phys. Sci. 162(909), 145–154 (1937)

    MATH  Google Scholar 

  13. Crassidis, J.L., Markley, F.L.: Unscented filtering for spacecraft attitude estimation. J. Guid. Control Dyn. 26(4), 536–542 (2003)

    Article  Google Scholar 

  14. Dambreville, S., Sandhu, R., Yezzi, A., Tannenbaum, A.: A geometric approach to joint 2d region-based segmentation and 3d pose estimation using a 3d shape prior. SIAM J. Imag. Sci. 3(1), 110–132 (2010)

    Article  MathSciNet  Google Scholar 

  15. Darling, J.E., DeMars, K.J.: Uncertainty propagation of correlated quaternion and euclidean states using partially-conditioned gaussian mixtures. In: 2016 19th international conference on information fusion (FUSION), pp 1805–1812. IEEE (2016)

  16. Fallaize, C.J., Kypraios, T.: Exact bayesian inference for the bingham distribution. Stat. Comput. 26(1-2), 349–360 (2016)

    Article  MathSciNet  Google Scholar 

  17. Finkelstein, D., Jauch, J.M., Schiminovich, S., Speiser, D.: Foundations of quaternion quantum mechanics. J. Math. Phys. 3(2), 207–220 (1962)

    Article  MathSciNet  Google Scholar 

  18. Flury, T., Shephard, N.: Bayesian inference based only on simulated likelihood: particle filter analysis of dynamic economic models. Econ. Theory 27(05), 933–956 (2011)

    Article  MathSciNet  Google Scholar 

  19. Forsyth, D.A., Ponce, J.: Computer vision: a modern approach, Prentice Hall Professional Technical Reference (2002)

  20. Gilitschenski, I., Kurz, G., Julier, S.J., Hanebeck, U.D.: Unscented orientation estimation based on the bingham distribution. IEEE Trans. Autom. Control 61(1), 172–177 (2016)

    Article  MathSciNet  Google Scholar 

  21. Glover, J., Kaelbling, L.P.: Tracking 3-d rotations with the quaternion bingham filter. Computer Science and Artificial Intelligence Laboratory - Technical Report (2013)

  22. Glover, J., Kaelbling, L.P.: Tracking the spin on a ping pong ball with the quaternion bingham filter. In: 2014 IEEE international conference on robotics and automation (ICRA), pp 4133–4140. IEEE (2014)

  23. Glover, J., Bradski, G., Rusu, R.B.: Monte Carlo pose estimation with quaternion kernels and the bingham distribution. In: Robotics: science and systems, vol. 7, p 97 (2012)

  24. Golub, G.H., Van Loan, C.F.: Matrix computations. Johns Hopkins University Press, Baltimore and London (1996)

    MATH  Google Scholar 

  25. Hastings, W.K.: Monte carlo sampling methods using markov chains and their applications. Biometrika 57(1), 97–109 (1970)

    Article  MathSciNet  Google Scholar 

  26. Inc I: https://insitu.com/information-delivery/unmanned-systems/scaneagle#3 (2016)

  27. Jammalamadaka, S.R., Sengupta, A.: Topics in circular statistics, vol. 5. World Scientific, Hackensack (2001)

    Book  Google Scholar 

  28. Kantas, N., Doucet, A., Singh, S.S., Maciejowski, J., Chopin, N.: On particle methods for parameter estimation in state-space models. Stat. Sci. 30(3), 328–351 (2015)

    Article  MathSciNet  Google Scholar 

  29. Kong, W., Zhang, D., Wang, X., Xian, Z., Zhang, J.: Autonomous landing of an Uav with a ground-based actuated infrared stereo vision system. In: 2013 IEEE/RSJ international conference on intelligent robots and systems (IROS), pp 2963–2970. IEEE (2013)

  30. Kong, W., Zhou, D., Zhang, Y., Zhang, D., Wang, X., Zhao, B., Yan, C., Shen, L., Zhang, J.: A ground-based optical system for autonomous landing of a fixed wing uav. In: 2014 IEEE/RSJ international conference on intelligent robots and systems, pp 4797–4804. IEEE (2014)

  31. Kraft, E.: A quaternion-based unscented kalman filter for orientation tracking. In: Proceedings of the sixth international conference of information fusion, vol. 1, pp 47– 54 (2003)

  32. Kurz, G., Gilitschenski, I., Hanebeck, U.D.: Recursive nonlinear filtering for angular data based on circular distributions. In: American control conference (ACC), 2013, pp 5439–5445. IEEE (2013)

  33. Kurz, G., Gilitschenski, I., Hanebeck, U.D.: Nonlinear measurement update for estimation of angular systems based on circular distributions. In: American control conference (ACC), 2014, pp 5694–5699. IEEE (2014)

  34. Kurz, G., Gilitschenski, I., Julier, S., Hanebeck, U.D.: Recursive bingham filter for directional estimation involving 180 degree symmetry. J. Adv. Inf. Fus. 9(2), 90–105 (2014)

    Google Scholar 

  35. Lee, T., McClamroch, N.H., Leok, M.: Attitude maneuvers of a rigid spacecraft in a circular orbit. In: 2006 american control conference, pp 6–pp. IEEE (2006)

  36. Lee, T., Leok, M., Mcclamroch, N.H.: Geometric tracking control of a quadrotor Uav on Se (3). In: 49th IEEE conference on decision and control (CDC), pp 5420–5425. IEEE (2010)

  37. Lefferts, E.J., Markley, F.L., Shuster, M.D.: Kalman filtering for spacecraft attitude estimation. J. Guid, Control Dyn. 5(5), 417–429 (1982)

    Article  Google Scholar 

  38. Lepetit, V., Fua, P., et al.: Monocular model-based 3d tracking of rigid objects: a survey. Found. Trends\({\circledR }\) Comput. Graph. Vis. 1(1), 1–89 (2005)

    Article  Google Scholar 

  39. Liu, B., Cheng, S., Shi, Y.: Particle filter optimization: a brief introduction, pp 95–104. Springer International Publishing, Cham (2016). https://doi.org/10.1007/978-3-319-41000-5_10

    Google Scholar 

  40. Liu, J., West, M.: Combined parameter and state estimation in simulation-based filtering. In: Sequential Monte Carlo methods in practice, pp 197–223. Springer (2001)

  41. Liu, J., West, M.: Combined parameter and state estimation in simulation-based filtering, pp 197–223. Springer, Berlin (2001)

    MATH  Google Scholar 

  42. Mardia, K.: Characterizations of directional distributions, pp 365–385. Springer, Berlin (1975)

    Google Scholar 

  43. Mardia, K.V., Jupp, P.E.: Directional statistics, vol. 494. Wiley, Hoboken (2000)

    MATH  Google Scholar 

  44. Markley, F., Berman, N., Shaked, U.: Deterministic Ekf-Like estimator for spacecraft attitude estimation. In: American control conference, 1994, vol. 1, pp 247–251. IEEE (1994)

  45. Markley, F.L., Crassidis, J.L.: Fundamentals of spacecraft attitude determination and control, vol. 33. Springer, Berlin (2014)

    Book  Google Scholar 

  46. Okuma, K., Taleghani, A., De Freitas, N., Little, J.J., Lowe, D.G.: A boosted particle filter: Multitarget detection and tracking. In: European conference on computer vision, pp 28–39. Springer (2004)

  47. Okuma, K., Taleghani, A., Freitas, N.d., Little, J.J., Lowe, D.G.: A boosted particle filter: Multitarget detection and tracking. Comput. Vis. ECCV 2004, 28–39 (2004)

    MATH  Google Scholar 

  48. Pauwels, K., Rubio, L., Diaz, J., Ros, E.: Real-time model-based rigid object pose estimation and tracking combining dense and sparse visual cues. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2347–2354 (2013)

  49. Pessanha Santos, N., Melicio, F., Lobo, V., Bernardino, A.: A ground-based vision system for uav pose estimation. Int. J. Mechatron. Robot. (IJMR) - UNSYSdigital Int. J. 1(4), 7 (2014). http://ojs.unsysdigital.com/index.php/ijrm/article/view/180

    Google Scholar 

  50. Pessanha Santos, N., Lobo, V., Bernardino, A.: A ground-based vision system for uav tracking. In: OCEANS 2015 - Genova (2015)

  51. Pessanha Santos, N., Lobo, V., Bernardino, A.: Particle filtering based optimization applied to 3d model-based estimation for uav pose estimation. In: OCEANS 2017 - Aberdeen (2017)

  52. Pessanha Santos, N., Lobo, V., Bernardino, A.: 3d model-based estimation for uav tracking. In: OCEANS 2018 - Charleston (2018)

  53. Pessanha Santos, N., Lobo, V., Bernardino, A.: 3d model-based uav pose estimation using gpu. In: OCEANS 2019 MTS/IEEE SEATTLE, pp 1–6. IEEE (2019)

  54. Pessanha Santos, N., Lobo, V., Bernardino, A.: Unmanned aerial vehicle tracking using a particle filter based approach. In: 2019 IEEE underwater technology (UT) - Kaohsiung. IEEE (2019)

  55. Powell, M.: Direct search algorithms for optimization calculations, Acta Numer., 287–336 (1998)

  56. Prisacariu, V.A., Reid, I.D.: Pwp3d: Real-time segmentation and tracking of 3d objects. Int. J. Comput. Vis. 98(3), 335–354 (2012)

    Article  MathSciNet  Google Scholar 

  57. Pritchard, J.K., Seielstad, M.T., Perez-Lezaun, A., Feldman, M.W.: Population growth of human y chromosomes: a study of y chromosome microsatellites. Molecul. Biol. Evol. 16(12), 1791–1798 (1999)

    Article  Google Scholar 

  58. Redmon, J., Farhadi, A.: Yolov3: An incremental improvement. arXiv:180402767 (2018)

  59. Ross, S.: Introduction to probability models. Academic Press, Cambridge (2010)

    MATH  Google Scholar 

  60. Rui, Y., Chen, Y.: Better proposal distributions: object tracking using unscented particle filter. In: CVPR, vol. 2, pp 786–793 (2001)

  61. Sanders, J., Kandrot, E.: CUDA by example: an introduction to general-purpose GPU programming. Addison-Wesley Professional, Boston (2010)

    Google Scholar 

  62. Seo, B.K., Wuest, H.: A direct method for robust model-based 3d object tracking from a monocular rgb image. In: European conference on computer vision, pp 551–562. Springer (2016)

  63. Sigges, F., Baum, M., Hanebeck, U.D.: A likelihood-free particle filter for multi-obiect tracking. In: 2017 20th international conference on information fusion (Fusion), pp 1–5. IEEE (2017)

  64. Smit, S.J.A.: Autonomous landing of a fixed-wing unmanned aerial vehicle using differential Gps. Thesis, Stellenbosch University (2013)

  65. Stachniss, C., Hähnel, D., Burgard, W., Grisetti, G.: On actively closing loops in grid-based fastslam. Adv. Robot. 19(10), 1059–1079 (2005)

    Article  Google Scholar 

  66. Tjaden, H., Schwanecke, U., Schomer, E.: Real-time monocular pose estimation of 3d objects using temporally consistent local color histograms. In: Proceedings of the IEEE international conference on computer vision, pp 124–132 (2017)

  67. Van Der Merwe, R., Doucet, A., De Freitas, N., Wan, E.: The unscented particle filter. In: Advances in neural information processing systems, pp 584–590 (2001)

  68. Vermaak, J., Doucet, A., Perez, P.: Maintaining multi-modality through mixture tracking. In: Proceedings of the ninth IEEE international conference on computer vision (ICCV 2003), vol. 2, p 1110 (2003)

  69. Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of the 2001 IEEE computer society conference on computer vision and pattern recognition, 2001. CVPR 2001, vol. 1, pp 511–518. IEEE (2001)

  70. Wenzel, K.E., Masselli, A., Zell, A.: Automatic take off, tracking and landing of a miniature uav on a moving carrier vehicle. J. Intell. Robot. Sys. 61(1-4), 221–238 (2011)

    Article  Google Scholar 

  71. Wilt, N.: The cuda handbook: A comprehensive guide to gpu programming. Pearson Education, London (2013)

    Google Scholar 

  72. Zhao, Y.J., Pei, H.L.: Improved vision-based algorithm for unmanned aerial vehicles autonomous landing. Appl. Mech. Mater. 273, 560–565 (2013)

    Article  Google Scholar 

  73. Zhong, L., Zhang, L.: A robust monocular 3d object tracking method combining statistical and photometric constraints. Int. J. Comput. Vis. 127(8), 973–992 (2019)

    Article  Google Scholar 

  74. Zhong, L., Lu, M., Zhang, L.: A direct 3d object tracking method based on dynamic textured model rendering and extended dense feature fields. IEEE Trans. Circ. Sys. Video Technol. 28(9), 2302–2315 (2018)

    Article  Google Scholar 

  75. Zhou, E., Chen, X.: Sequential monte carlo simulated annealing. J. Global Optim. 55(1), 101–124 (2013). https://doi.org/10.1007/s10898-011-9838-3

    Article  MathSciNet  Google Scholar 

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Authors and Affiliations

  1. Institute for Systems and Robotics (ISR), University of Lisbon, Av. Rovisco Pais 1, 1049-001, Lisbon, Portugal

    Nuno Pessanha Santos & Alexandre Bernardino

  2. Portuguese Navy Research Center (CINAV), Escola Naval, Base Naval de Lisboa, 2810-001, Almada, Portugal

    Nuno Pessanha Santos & Victor Lobo

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  1. Nuno Pessanha Santos
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  2. Victor Lobo
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  3. Alexandre Bernardino
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The first author entirely performed this work during his Ph.D. studies and was guided by the second and third authors.

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Correspondence to Nuno Pessanha Santos.

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Pessanha Santos, N., Lobo, V. & Bernardino, A. Unscented Particle Filters with Refinement Steps for UAV Pose Tracking. J Intell Robot Syst 102, 52 (2021). https://doi.org/10.1007/s10846-021-01409-y

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