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Reduction of the uncertainty in feature tracking

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Abstract

It is difficult to establish feature correspondences between distant viewpoints for panoramic images. For reliable navigation and development a human-like capability of interaction with the surrounding environment, we need a method of reduction of the uncertainty in feature tracking. To obtain a method of reduction of the uncertainty in feature tracking, we propose to use an algorithm for the problem of the longest common subsequence for a set of circular strings. We consider an explicit reduction from the problem of the longest common subsequence for a set of circular strings to the satisfiability problem. This reduction allows to obtain an efficient algorithm for finding the longest common subsequence for a set of circular strings. We present a general scheme of the method of reduction of the uncertainty in feature tracking. We considered the visual homing task to demonstrate the capabilities of our approach to solve the problem of reduction of the uncertainty in feature tracking. We present experimental results for the method of reduction of the uncertainty in feature tracking and novel robot visual homing methods.

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References

  1. Davison AJ (2003) Real-Time simultaneous localisation and mapping with a single camera. In: Proceedings of the ninth IEEE international conference on computer vision. IEEE Computer Society, Washington, pp 1403–1410

  2. Davison AJ, Reid ID, Molton ND, Stasse O (2007) MonoSLAM: real-time single camera SLAM. IEEE Trans Pattern Anal Mach Intell 29:1052–1067

    Article  Google Scholar 

  3. Hayet JB, Lerasle F, Devy M (2007) A visual landmark framework for mobile robot navigation. Image Vis Comput 25:1341–1351

    Article  Google Scholar 

  4. Marinho LB, Almeida JS, Souza JWM, Albuquerque VHC, Filho PPR (2017) A novel mobile robot localization approach based on topological maps using classification with reject option in omnidirectional images. Expert Syst Appl 72:1–17

    Article  Google Scholar 

  5. Briggs AJ, Li Y, Scharstein D, Wilder M (2006) Robot navigation using 1D panoramic images. In: Proceedings of the IEEE international conference on robotics and automation. IEEE Press, Piscataway, pp 2679–2685

  6. Collins RT, Liu Y, Leordeanu M (2005) Online selection of discriminative tracking features. IEEE Trans Pattern Anal Mach Intell 27:1631–1643

    Article  Google Scholar 

  7. Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60:91–110

    Article  Google Scholar 

  8. Yilmaz A, Javed O, Shah M (2006) Object tracking: a survey. ACM Comput Surv 38:13

    Article  Google Scholar 

  9. Lamon P, Nourbakhsh I, Jensen B, Siegwart R (2001) Deriving and matching image fingerprint sequences for mobile robot localization. In: Proceedings of the IEEE international conference on robotics and automation. IEEE Press, Piscataway, pp 1609–1614

  10. Selman B, Levesque H, Mitchell D (1992) A new method for solving hard satisfiability problems. In: Proceedings of the tenth national conference on artificial intelligence. AAAI Press, San Jose, pp 440–446

  11. Xie L, Zeng J (2010) The performance analysis of artificial physics optimization algorithm driven by different virtual forces. ICIC-EL 4:239–244

    Google Scholar 

  12. Wang Y-J, Lin C-T (1998) Runge Kutta neural network for identification of continuous systems. In: Proceedings of the 1998 IEEE international conference on systems, man, and cybernetics. IEEE Press, Piscataway, pp 3277–3282

  13. Arunkumar GK, Sabnis A, Vachhani L (2018) Robust steering control for autonomous homing and its application in visual homing under practical conditions. J Intell Robot Syst 89:403–419

    Article  Google Scholar 

  14. Liu M, Pradalier C, Siegwart R (2013) Visual homing from scale with an uncalibrated omnidirectional camera. IEEE Trans Robot 29:1353–1365

    Article  Google Scholar 

  15. Möller R, Vardy A (2006) Local visual homing by matched filter descent in image distances. Biol Cybern 95:413–430

    Article  MathSciNet  Google Scholar 

  16. Ramisa A, Goldhoorn A, Aldavert D, Toledo R, De Mantaras R L (2011) Combining invariant features and the ALV homing method for autonomous robot navigation based on panoramas. J Intel Robot Syst 64:625–649

    Article  Google Scholar 

  17. Tron R, Daniilidis K (2014) An optimization approach to bearing-only visual homing with applications to a 2-D unicycle model. In: Proceedings of IEEE international conference on robotics and automation. IEEE Press, Piscataway, pp 4235–4242

  18. Zeil J, Hofmann MI, Chahl JS (2003) Catchment areas of panoramic snapshots in outdoor scenes. J Opt Soc Amer 20:450–469

    Article  Google Scholar 

  19. Aranda M, López-Nicolás G, Sagüés C (2017) Angle-based navigation using the 1D trifocal tensor. In: Aranda M, López-Nicolás G, Sagüés C (eds) Control of multiple robots using vision sensors. Springer International Publishing AG, Cham, pp 19–51

    Chapter  Google Scholar 

  20. Yu S-E, Kim D (2011) Image-based homing navigation with landmark arrangement matching. Inf Sci 181:3427–3442

    Article  Google Scholar 

  21. Muller MM, Bertrand OJN, Differt D, Egelhaaf M (2018) The problem of home choice in skyline-based homing. PLoS One 13:e0194070

    Article  Google Scholar 

  22. Stauffer C, Grimson WEL (2000) Learning patterns of activity using real-time tracking. IEEE Trans Pattern Anal Mach Intell 22:747–757

    Article  Google Scholar 

  23. Latecki L, Megalooikonomou V, Miezianko R, Pokrajac D (2006) Using spatiotemporal blocks to reduce the uncertainty in detecting and tracking moving objects in video. IJISTA 1:376–392

    Article  Google Scholar 

  24. Lucas B, Kanade T (1981) An iterative image registration technique with an application to stereo vision. In: Proceedings of the international joint conference on artificial intelligence. Morgan Kaufmann Publishers Inc, San Francisco, pp 674–679

  25. Black MJ (1992) Robust incremental optical flow. Yale University, New Haven

    Google Scholar 

  26. Bergen JR, Anandan P, Hanna KJ, Hingorani R (1992) Hierarchical model-based motion estimation. In: Sandini G (ed) Computer vision. Springer, Berlin, pp 237–252

    Chapter  Google Scholar 

  27. Hager GD, Belhumeur PN (1998) Efficient region tracking with parametric models of geometry and illumination. IEEE Trans Pattern Anal Mach Intell 20:1025–1039

    Article  Google Scholar 

  28. Swaminathan R, Kang SB, Szeliski R, Criminisi A, Nayar S (2002) On the motion and appearance of specularities in image sequences. In: Heyden A, Sparr G, Nielsen M, Johansen P (eds) Computer vision. Springer, Berlin, pp 508–523

    Chapter  Google Scholar 

  29. Leonard JJ, Durrant-Whyte HF (1992) Directed sonar sensing for mobile robot navigation. Springer, New York

    Book  Google Scholar 

  30. Fox D, Burgard W, Dellaert F, Thrun S (1999) Monte carlo localization: efficient position estimation for mobile robots. In: Proceedings of the national conference on artificial intelligence. American Association for Artificial Intelligence, Menlo Park, pp 343–349

  31. Olson CF (2000) Probabilistic self-localization for mobile robots. IEEE Trans Robot Autom 16:55–66

    Article  Google Scholar 

  32. Soatto S, Brockett R (1998) Optimal structure from motion: local ambiguities and global estimates. In: IEEE conference on computer vision and pattern recognition. IEEE Press, Piscataway, pp 282–288

  33. Thompson P, Sukkarieh S (2006) Tracking multiple features including cross-feature correlations, with observation parameter uncertainties. In: 9th international conference on information fusion. IEEE Press, Piscataway, pp 1–8

  34. Lin X, Bar-Shalom Y, Kirubarajan T (2005) Multisensor-multitarget bias estimation for general asynchronous sensors. IEEE Trans Aerosp Electron Syst 41:899–921

    Article  Google Scholar 

  35. Shea PJ, Zadra T, Klamer D, Frangione E, Brouillard R, Kastella K (2000) Precision tracking of ground targets. In: IEEE aerospace conference proceedings. IEEE Press, Piscataway, pp 473–482

  36. Novoselov RY, Herman SM, Gadaleta SM, Poore AB (2005) Mitigating the effects of residual biases with Schmidt-Kalman filtering. In: 8th international conference on information fusion. IEEE Press, Piscataway, pp 1–8

  37. Sukkarieh S, Nettleton E, Kim J-H, Ridley M, Goktogan A, Durrant-Whyte H (2003) The ANSER project: data fusion across multiple uninhabited air vehicles. Int J Robot Res 22:505–539

    Article  Google Scholar 

  38. Julier SJ, Uhlmann JK (2017) General decentralized data fusion with covariance intersection. In: Liggins M, Hall D, Llinas J (eds) Handbook of multisensor data fusion: theory and practice. CRC Press, Broken Sound Parkway, pp 319–344

  39. Di Marco M, Garulli A, Giannitrapani A, Vicino A (2004) A set theoretic approach to dynamic robot localization and mapping. Auton Robot 16:23–47

    Article  Google Scholar 

  40. Rybski PE, Roumeliotis S, Gini M, Papanikopoulos N (2008) Appearance-based mapping using minimalistic sensor models. Auton Robot 24:229–246

    Article  Google Scholar 

  41. Jebari I, Bazeille S, Filliat D (2012) Combined vision and frontier-based exploration strategies for semantic mapping. In: Yang D (ed) Informatics in control, automation and robotics. Springer, Berlin, pp 237–244

    Chapter  Google Scholar 

  42. Gaussier P, Revel A, Banquet J, Babeau V (2002) From view cells and place cells to cognitive map learning: processing stages of the hippocampal system. Biol Cybern 86:15–28

    Article  Google Scholar 

  43. Cuperlier N, Quoy M, Gaussier P, Giovanangelli C (2006) Navigation and planning in an unknown environment using vision and a cognitive map. In: Christensen H I (ed) European robotics symposium 2006. Springer, Berlin, pp 48–53

  44. Spaan MTJ, Vlassis N (2004) A point-based POMDP algorithm for robot planning. In: IEEE international conference on robotics and automation. IEEE Press, Piscataway, pp 2399–2404

  45. Di Marco M, Garulli A, Giannitrapani A, Vicino A (2001) Set membership pose estimation of mobile robots based on angle measurements. In: Proceedings of the 40th IEEE conference on decision and control. IEEE Press, Piscataway, pp 3734–3739

  46. Hanebeck UD, Schmidt G (1996) Set theoretical localization of fast mobile robots using an angle measurement technique. In: Proceedings—IEEE international conference on robotics and automation. IEEE Press, Piscataway, pp 1387–1394

  47. Argyros AA, Bekris KE, Orphanoudakis SC, Kavraki LE (2005) Robot homing by exploiting panoramic vision. Auton Robot 19:7–25

    Article  Google Scholar 

  48. Gupta M, Kumar S, Behera L, Subramanyam VK (2017) A novel fusion framework for robust human tracking by a service robot. Robot Auton Syst 94:134–147

    Article  Google Scholar 

  49. Kang T, Mo Y, Pae D, Ahn C, Lim M (2017) Robust visual tracking framework in the presence of blurring by arbitrating appearance- and feature-based detection. Measurement 95:50–69

    Article  Google Scholar 

  50. Walia GS, Raza S, Gupta A, Asthana R, Singh K (2017) A novel approach of multi-stage tracking for precise localization of target in video sequences. Expert Syst Appl 78:208–224

    Article  Google Scholar 

  51. Xiao J, Stolkin R, Leonardis A (2017) Dynamic multi-level appearance models and adaptive clustered decision trees for single target tracking. Pattern Recogn 69:169–183

    Article  Google Scholar 

  52. Aliakbarpour H, Tahri O, Araujo H (2014) Visual servoing of mobile robots using non-central catadioptric cameras. Robot Auton Syst 62:1613–1622

    Article  Google Scholar 

  53. Kundu AS, Mazumder O, Dhar A, Lenka PK, Bhaumik S (2017) Scanning camera and augmented reality based localization of omnidirectional robot for indoor application. Procedia Comput Sci 105:27–33

    Article  Google Scholar 

  54. Bouhali M, Shamani F, Dahmane ZE, Belaidi A, Nurmi J (2017) FPGA applications in unmanned aerial vehicles—a review. In: Wong S, Beck A, Bertels K, Carro L (eds) Applied reconfigurable computing. Springer, Cham, pp 217–228

    Google Scholar 

  55. Nüchter A, Feyzabadi S, Qiu D, May S (2011) SLAM à la carte – GPGPU for globally consistent scan matching. In: Proceedings of the 5th European conference on mobile robots. Learning Systems Lab, Örebro, pp 271–276

  56. Guevara AE, Hoak A, Bernal JT, Medeiros H (2016) Vision-based self-contained target following robot using Bayesian data fusion. In: Bebis G, Boyle R, Parvin B, Koracin D, Porikli F, Skaff S, Entezari A, Min J, Iwai D, Sadagic A, Scheidegger C, Isenberg T (eds) Advances in visual computing. Springer, Cham, pp 846–857

  57. Besl PJ, McKay ND (1992) A method for registration of 3-D shapes. IEEE Trans Pattern Anal Mach Intell 14:239–256

    Article  Google Scholar 

  58. Fischler MA, Bolles RC (1981) Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun ACM 24:381–395

    Article  MathSciNet  Google Scholar 

  59. Shi Q, Li C, Wang C, Luo H, Huang Q, Fukuda T (2017) Design and implementation of an omnidirectional vision system for robot perception. Mechatronics 41:58–66

    Article  Google Scholar 

  60. Ojala T, Pietikäinen M, Mäenpää T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24:971–987

    Article  Google Scholar 

  61. Rassem TH, Khoo BE (2014) Completed local ternary pattern for rotation invariant texture classification. Sci World J. https://doi.org/10.1155/2014/373254

    Article  Google Scholar 

  62. Zhao Y, Huang DS, Jia W (2012) Completed local binary count for rotation invariant texture classification. IEEE Trans Image Process 21:4492–4497

    Article  MathSciNet  Google Scholar 

  63. Guo W, Feng Z, Ren X (2017) Object tracking using local multiple features and a posterior probability measure. Sensors. https://doi.org/10.3390/s17040739

    Article  Google Scholar 

  64. Clement L, Kelly J, Barfoot TD (2017) Robust monocular visual teach and repeat aided by local ground planarity and color-constant imagery. J Field Robot 34:74–97

    Article  Google Scholar 

  65. López-Nicolás G, Guerrero JJ, Sagüés C (2017) Multiple homographies with omnidirectional vision for robot homing. Robot Auton Syst 58:773–783

    Article  Google Scholar 

  66. Zheng N, Xue J (2009) Statistical learning and pattern analysis for image and video processing. Springer, London

    Book  Google Scholar 

  67. Oikonomopoulos A, Patras I, Pantic M, Paragios N (2007) Trajectory-based representation of human actions. In: Huang T S, Nijholt A, Pantic M, Pentland A (eds) Artifical intelligence for human computing. Springer, Berlin, pp 133–154

  68. Needleman SB, Wunsch CD (1970) A general method applicable to the search for similarities in the amino acid sequence of two proteins. J Mol Biol 48:443–453

    Article  Google Scholar 

  69. Crochemore M, Landau GM, Ziv-Ukelson M (2003) A subquadratic sequence alignment algorithm for unrestricted score matrices. SIAM J Comput 32:1654–1673

    Article  MathSciNet  Google Scholar 

  70. Schmidt JP (1998) All highest scoring paths in weighted grid graphs and its application to finding all approximate repeats in strings. SIAM J Comput 27:972–992

    Article  MathSciNet  Google Scholar 

  71. Tiskin A (2008) Semi-local longest common subsequences in subquadratic time. J Discrete Algorithms 6:570–581

    Article  MathSciNet  Google Scholar 

  72. Nicolas F, Rivals E (2007) Longest common subsequence problem for unoriented and cyclic strings. Theor Comput Sci 370:1–18

    Article  MathSciNet  Google Scholar 

  73. Gorbenko A, Popov V (2012) The set of parameterized k-covers problem. Theor Comput Sci 423:19–24

    Article  MathSciNet  Google Scholar 

  74. Neato Robotics web page. http://www.neatorobotics.com. Accessed 20 June 2017

  75. Tomatis N (1998) Vision feedback for mobile robots. Dissertation, Swiss Federal Institute of Technology

  76. Shi J, Tomasi C (1994) Good features to track. In: IEEE computer society conference on computer vision and pattern recognition. IEEE Press, Piscataway, pp 593–600

  77. Bay H, Ess A, Tuytelaars T, Van Gool L (2008) Speeded-up robust features (SURF). Comput Vis Image Underst 110:346–359

    Article  Google Scholar 

  78. Rosten E, Drummond T (2006) Machine learning for high-speed corner detection. In: Leonardis A, Bischof H, Pinz A (eds) Computer vision—ECCV, vol 2006. Springer, Berlin, pp 430–443

    Chapter  Google Scholar 

  79. Rosten E, Drummond T (2005) Fusing points and lines for high performance tracking. In: Proceedings of the tenth IEEE international conference on computer vision. IEEE Press, Piscataway, pp 1508–1515

  80. Harris CG, Stephens MJ (1988) A combined corner and edge detector. In: Proceedings of the 4th Alvey vision conference. BMVA, Manchester, pp 147–151

  81. Omnidirectional image database of virtual and real environment for mobile robot localization. http://lapisco.ifce.edu.br/?page_id=228. Accessed 20 June 2017

  82. Gorbenko A, Popov V (2012) On the problem of placement of visual landmarks. AMS 6:689–696

    MathSciNet  MATH  Google Scholar 

  83. Gorbenko A, Popov V (2012) Task-resource scheduling problem. Int J Autom Comput 9:429–441

    Article  Google Scholar 

  84. Gorbenko A, Popov V (2013) The force law design of artificial physics optimization for starting population selection for GSAT. ASTP 7:131–134

    Article  Google Scholar 

  85. SoftBank Robotics. https://www.ald.softbankrobotics.com/en. Accessed 20 June 2017

  86. Falotico E, Cauli N, Kryczka P, Hashimoto K, Berthoz A, Takanishi A, Dario P, Laschi C (2017) Head stabilization in a humanoid robot: models and implementations. Auton Robot 41:349–365

    Article  Google Scholar 

  87. Paolillo A, Faragasso A, Oriolo G, Vendittelli M (2017) Vision-based maze navigation for humanoid robots. Auton Robot 41:293–309

    Article  Google Scholar 

  88. Zahra SJ, Sulaiman R, Prabuwono AS, Kahaki SMM (2015) Improved descriptor for dynamic line matching in omnidirectional images. In: Proceedings—5th international conference on electrical engineering and informatics: bridging the knowledge between academic, industry, and community. IEEE Press, Piscataway, pp 138–142

  89. Yu S-E, Kim D (2011) Landmark vectors with quantized distance information for homing navigation. Adapt Behav 19:121–141

    Article  Google Scholar 

  90. Yu S-E, Lee C, Kim D (2012) Analyzing the effect of landmark vectors in homing navigation. Adapt Behav 20:337–359

    Article  Google Scholar 

  91. Lee C, Kim D (2016) A moment measure model of landmarks for local homing navigation. In: Tuci E, Giagkos A, Wilson M, Hallam J (eds) From animals to animats, vol 14. Springer, Cham, pp 126–137

    Chapter  Google Scholar 

  92. Lee C, Kim D (2016) A landmark vector approach using gray-colored information. In: Tuci E, Giagkos A, Wilson M, Hallam J (eds) From animals to animats, vol 14. Springer, Cham, pp 138–144

    Chapter  Google Scholar 

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Acknowledgements

The research of Anna Gorbenko was partially supported by the Ministry of Education and Science of the Russian Federation project “Combinatorial models in computer science and their applications”.

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  1. Department of Intelligent Systems and Robotics of the Regional Educational and Scientific Center of Intelligent Systems and Information Security, Institute of Natural Sciences and Mathematics, Ural Federal University, Lenin st., 51, 620083, Ekaterinburg, Russian Federation

    Anna Gorbenko & Vladimir Popov

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  1. Anna Gorbenko
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  2. Vladimir Popov
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Correspondence to Anna Gorbenko.

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Gorbenko, A., Popov, V. Reduction of the uncertainty in feature tracking. Appl Intell 48, 4626–4645 (2018). https://doi.org/10.1007/s10489-018-1236-9

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