http://iet.metastore.ingenta.com
1887

Appearance-based approach to hybrid metric-topological simultaneous localisation and mapping

Appearance-based approach to hybrid metric-topological simultaneous localisation and mapping

For access to this article, please select a purchase option:

Buy article PDF
£12.50
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Intelligent Transport Systems — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

In this study a unified framework to carry out the simultaneous localisation and mapping of a mobile robot combining metric and topological techniques is presented. The robot moves in a real indoor environment and the algorithm makes use of the information provided by an omnidirectional camera mounted on the robot and its internal odometry. The hybrid approach consists in constructing simultaneously two maps of the environment, one metric and other topological with relationships between them which are updated in each step. The robot goes through the environment to build up a map while continuously captures images. To build the topological map the most relevant information from the scenes is extracted using a global appearance descriptor. A new node is added to the map when the appearance between two images is sufficiently different. Also, the authors check if there is a loop closure with a previous node. At the same time, a metrical map of the environment is computed. With this aim, the authors estimate the position of the robot when it captures a new image using a Monte–Carlo algorithm. The authors show how it is possible to obtain a reasonable performance both in time and accuracy in an indoor environment, when the involved parameters are properly tuned.

References

    1. 1)
      • 1. Smith, R., Self, M., Cheeseman, P.: ‘A stochastic map for uncertain spatial relationships’. Proc. Fourth Int. Symp.. Cambridge, MA, USA, 1988, pp. 467474..
    2. 2)
      • 2. Tully, S., Moon, H., Kantor, G., Choset, H.: ‘Iterated filters for bearing-only SLAM’. Proc. IEEE Int. Conf. on Robotics and Automation (ICRA). Pasadena, California, USA, 2008, pp. 14421448.
    3. 3)
    4. 4)
    5. 5)
      • 5. Diosi, A., Kleeman, L.: ‘Advanced sonar and laser range finder fusion for simultaneous localization and mapping’. Proc. of IEEE/RSJ International Conference on Intelligent Robots and Systems, vol. 2, Sendai, Japan, 2004, pp. 18541859.
    6. 6)
      • 6. Angeli, A., Doncieux, S., Meyer, J., Filliat, D.: ‘Visual topological SLAM and global localization’. Proc. of IEEE International Conference on Robotics and Automation, Piscataway, NJ, USA, 2009, pp. 20292034.
    7. 7)
    8. 8)
      • 8. Romero, A., Cazorla, M.: ‘Topological SLAM using omnidirectional images: merging feature detectors and graph-matching’, in: Blanc-Talon, J., Bone, D., Philips, W., Popescu, D., Scheunders, P., (Eds.), ‘Advanced concepts for intelligent vision systems’, (6474LNCS). Springer, Berlin, Heidelberg, 2010, pp. 464475.
    9. 9)
      • 9. Motard, E., Raducanu, B., Cadenat, V., Vitrià, J.: ‘Incremental on-line topological map learning for a visual homing application’. Proc. of IEEE International Conference on Robotics and Automation, 2007, pp. 20492054.
    10. 10)
    11. 11)
      • 11. Tully, S., Moon, H., Morales, D., Kantor, G., Choset, H.: ‘Hybrid localization using the hierarchical atlas’. Proc. of IEEE/RSJ International Conference on Intelligent Robots and Systems, 2007, pp. 28572864.
    12. 12)
      • 12. Blanco, J.L., Fernandez, J.A., Gonzalez, J.: ‘A new approach for large-scale localization and mapping: hybrid metric-topological SLAM’. Proc. of the IEEE International Conference on Robotics and Automation, 2007, pp. 20612067.
    13. 13)
    14. 14)
      • 14. Montemerlo, M., Thrun, S.: ‘Simultaneous localization and mapping with unknown data association using FastSLAM’. Proc. of IEEE International Conference on Robotics and Automation, Taipei, Taiwan, 2003, vol. 2, pp. 19851991.
    15. 15)
      • 15. Hähnel, D., Burgard, W., Fox, D., Thrun, S.: ‘An efficient FastSLAM algorithm for generating maps of large-scale cyclic environments from raw laser range measurements’. Proc. of IEEE/RSJ International Conference on Intelligent Robots and Systems, Las Vegas, NV, USA, 2003, pp. 206211.
    16. 16)
    17. 17)
      • 17. Gil, A., Reinoso, O., Pay, L., Ballesta, M., Pedrero, J.: ‘Managing data association in visual SLAM using SIFT features’, International Journal of Factory Automation, Robotics and Soft Computing, 2007, 1, pp. 179184.
    18. 18)
      • 18. Lui, W., Jarvis, R.: ‘A pure vision-based approach to topological SLAM’. Proc. IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, Taipei, Taiwan, 2010, pp. 37843791.
    19. 19)
    20. 20)
      • 20. Tully, S., Kantor, G., Choset, H., Werner, F.: ‘A multi-hypothesis topological SLAM approach for loop closing on edge-ordered graphs’. Proc. IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, Piscataway, NJ, USA, 2009, pp. 49434948.
    21. 21)
    22. 22)
    23. 23)
      • 23. Murillo, A.C., Guerrero, J.J., Sagüés, C.: ‘SURF features for efficient robot localization with omnidirectional images’. Proc. IEEE Int. Conf. on Robotics and Automation, San Diego, CA, USA, 2007, pp. 39013907.
    24. 24)
      • 24. Valgren, C., Lilienthal, A.: ‘SIFT, SURF and seasons: long-term outdoor localization using local features’. Proc. European Conf. on Mobile Robots, Freiburg, Germany, 2007, pp. 253258.
    25. 25)
    26. 26)
      • 27. Barfoot, T.D.: ‘Online Visual Motion Estimation using FastSLAM with SIFT Features’. IEEE/RSJ Int. Conf. on Intelligent Robots and Systems. Edmonton, s, 2005, pp. 579585.
    27. 27)
      • 28. Gil, A., Reinoso, O., Vicente, A., Fernández, C., Payá, L.: ‘Monte Carlo localization using SIFT features’. Pattern Recognition and Image Analysis, (3522> LCNS), 2005, pp. 623630.
    28. 28)
      • 29. Harris, C.G., Stephens, M.: ‘A combined corner and edge detector’. Proc. of Alvey Vision Conference, 1998, pp. 147151.
    29. 29)
      • 30. Jogan, M., Leonardis, A.: ‘Robust localization using eigenspace of spinning-images’. Proc. IEEE Workshop on Omnidirectional Vision. Hilton Head Island, USA, 2000, pp. 3744.
    30. 30)
      • 31. Paya, L., Fernandez, L., Reinoso, O., Gil, A., Ubeda, D.: ‘Appearance-based dense maps creation. Comparison of compression techniques with panoramic images’. Proc. Int. Conf. on Informatics in Control, Automation and Robotics, Milan, Italy, 2009, pp. 238246.
    31. 31)
      • 32. Pretto, A., Menegatti, E., Pagello, E., Jitsukawa, Y., Ueda, R., Arai, T.: ‘Toward image-based localization for AIBO using wavelet transform’. Proc. Artificial Intelligence and Human-Oriented Computing, Berlin, Heidelberg, 2007, pp. 831838.
    32. 32)
    33. 33)
      • 34. Rossi, F., Ranganathan, A., Dellaert, F., Menegatti, E.: ‘Toward topological localization with spherical Fourier transform and uncalibrated camera’. Proc. Int. Conf. on Simulation, Modeling and Programming for Autonomous Robots, Venice, Italy, 2008, pp. 319330.
    34. 34)
    35. 35)
      • 36. Amorós, F., Payá, L., Reinoso, Ó., Fernández, L., Marín, J.M.: ‘Visual map building and localization with an appearance-based approach. Comparisons of techniques to extract information of panoramic images’. Proc. Int. Conf. on Informatics in Control, Automation and Robotics, Madeira, Portugal, 2010, pp. 423426.
    36. 36)
      • 37. Amorós, F., Payá, L., Reinoso, Ó., Jiménez, L.M.: ‘Comparison of global-appearance techniques applied to visual map building and localization’. Proc. Int. Conf. on Computer Vision Theory and Applications, Rome, Italy, 2012, vol. 2, pp. 395398.
    37. 37)
    38. 38)
    39. 39)
    40. 40)
      • 41. Wu, J., Christensen, H.I., Rehg, J.M.: ‘Visual place categorization: problem, dataset, and algorithm’. Proc. IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, St. Louis, USA, 2009, pp. 47634770.
    41. 41)
      • 42. Folkesson, J., Christensen, H.: ‘Outdoor exploration and SLAM using a compressed filter’. Proc. IEEE Int. Conf. on Robotics and Automation, Taipei, Taiwan, 2003, vol. 1, pp. 419426.
    42. 42)
      • 43. Gonzalez, R., Wintz, P.: ‘Digital image processing’ (Addison, 1987).
    43. 43)
    44. 44)
      • 45. Fernandez, L., Paya, L., Reinoso, O., Gil, A., Julia, M., Ballesta, M.: ‘Robust methods for robot localization under changing illumination conditions. Comparison of different filtering techniques’. Proc. Int. Conf. on Agents and Artificial Intelligence, Valencia, Spain, 2010, vol. 1, pp. 223228.
    45. 45)
      • 46. Valiente, D., Fernandez, L., Aparicio, A.G., Castello, L.P., Garcia, O.R.: ‘Visual Odometry through appearance and feature-based method with omnidirectional images’, J. Robot., 2012, 2012, pp. 130.
    46. 46)
      • 47. Fox, D., Burgard, W., Thrun, S.: ‘Markov localization for mobile robots in dynamic environments’, J. Artif. Intell. Res., 1999, 11, pp. 391427.
    47. 47)
    48. 48)
      • 49. Smith, A.F.M., Gelfand, A.E.: ‘Bayesian statistics without tears: a sampling-resampling perspective’, Am. Stat., 1992, 46, (2), pp. 8488.
    49. 49)
      • 50. ROS. http://www.ros.org, 2012.
    50. 50)
      • 51. Stachniss, C., Grisetti, G., Hähnel, D., Burgard, W.: ‘Improved Rao-Blackwellized mapping by adaptive sampling and active loop-closure’. Proc. Workshop on Self-Organization of Adaptive Behavior. Ilmenau, Germany, 2004, pp. 115.
    51. 51)
      • 52. Seber, G.: ‘Multivariate observations’ (Wiley Interscience, 1984).
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-its.2013.0086
Loading

Related content

content/journals/10.1049/iet-its.2013.0086
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address