Skip to main content
SpringerLink
Log in

Mobile Robot Localization: Where We Are and What Are the Challenges?

  • Conference paper
  • First Online:
Automation 2017 (ICA 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 550))

Included in the following conference series:

Abstract

This article surveys recent developments in the area of mobile robot localization. The focus is on indoor 3-D localization from vision and RGB-D data. We analyze three important aspects of the architecture of localization systems: perception, representation of the obtained data, and estimation of the robot trajectory from the internal representation of the outer environment. We attempt also to identify challenges and open problems in the domain. The analysis is illustrated by extensive references to the selected literature, as this paper was also conceived as a guide for those researchers, who want to enter the fascinating realm of SLAM for the first time.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    Open-source code available at https://github.com/LRMPUT/PUTSLAM/tree/release.

References

  1. Agarwal, P., Grisetti, G., Tipaldi, G., Spinello, L., Burgard, W., Stachniss, C.: Experimental analysis of dynamic covariance scaling for robust map optimization under bad initial estimates. In: Proceedings of the IEEE International Conference on Robotics & Automation, Hong Kong, pp. 3626–3631 (2014)

    Google Scholar 

  2. Ataer-Cansizoglu, E., Taguchi, Y.: Object detection and tracking in RGB-D SLAM via hierarchical feature grouping. In: IEEE/RSJ International Conference on Intelligent Robots & Systems, Daejeon, pp. 4164–4171 (2016)

    Google Scholar 

  3. Bachrach, A., Prentice, S., He, R., Henry, P., Huang, A., Krainin, M., Maturana, D., Fox, D., Roy, N.: Estimation, planning, and mapping for autonomous flight using an RGB-D camera in GPS-denied environments. Int. J. Robot. Res. 31(11), 1320–1343 (2012)

    Article  Google Scholar 

  4. Bailey, T., Durrant-Whyte, H.: Simultaneous localization and mapping: part II. IEEE Robot. Autom. Mag. 13(3), 108–117 (2006)

    Article  Google Scholar 

  5. Baker, S., Matthews, I.: Lucas-Kanade 20 years on: a unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004)

    Article  Google Scholar 

  6. Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: Speeded-up robust features (SURF). Comput. Vis. Image Underst. 110(3), 346–359 (2008)

    Article  Google Scholar 

  7. Bȩdkowski, J.: Qualitative Spatio-Temporal Representation and Reasoning for Robotic Applications, Academic Publishing House EXIT (2015)

    Google Scholar 

  8. Belter, D., Skrzypczyński, P.: Precise self-localization of a walking robot on rough terrain using parallel tracking an mapping. Industr. Robot Int. J. 40(3), 229–237 (2013)

    Article  Google Scholar 

  9. Belter, D., Nowicki, M., Skrzypczyński, P.: On the performance of pose-based RGB-D visual navigation systems. In: Cremers, D., Reid, I., Saito, H., Yang, M.-H. (eds.) ACCV 2014. LNCS, vol. 9004, pp. 407–423. Springer, Heidelberg (2015). doi:10.1007/978-3-319-16808-1_28

    Google Scholar 

  10. Belter, D., Nowicki, M., Skrzypczyński, P., Walas, K., Wietrzykowski, J.: Lightweight RGB-D SLAM system for search and rescue robots. In: Szewczyk, R., Zieliński, C., Kaliczyńska, M. (eds.) Progress in Automation, Robotics and Measuring Techniques. AISC, vol. 351, pp. 11–21. Springer, Cham (2015). doi:10.1007/978-3-319-15847-1_2

    Google Scholar 

  11. Belter, D., Nowicki, M., Skrzypczyński, P.: Accurate map-based RGB-D SLAM for mobile robots. In: Reis, L., et al. (eds.) Robot 2015: Advances in Robotics. AISC, vol. 418, pp. 533–545. Springer, Heidelberg (2016). doi:10.1007/978-3-319-27149-1_41

    Google Scholar 

  12. Belter, D., Nowicki, M., Skrzypczyński, P.: Improving accuracy of feature-based RGB-D SLAM by modeling spatial uncertainty of point features. In: Proceedings of the IEEE International Conference on Robotics and Automation, Stockholm, Sweden, pp. 1279–1284 (2016)

    Google Scholar 

  13. Belter, D., Kostusiak, A., Nowicki, M., Skrzypczyński, P.: Real-time SLAM from RGB-D data on a legged robot: an experimental study. In: Tokhi, M.O., Virk, G.S. (eds.) Advances in Cooperative Robotics, pp. 320–328. World-Scientific (2016)

    Google Scholar 

  14. Besl, P.J., McKay, N.D.: A method for registration of 3-D shapes. IEEE Trans. Pattern Anal. Mach. Intell. 14(2), 239–256 (1992)

    Article  Google Scholar 

  15. Borenstein, J., Everett, H.R., Feng, L.: Where am I? Sensors and methods for mobile robot positioning, Technical Report, University of Michigan (1996)

    Google Scholar 

  16. Castellanos, J.A., Tardós, J.D.: Mobile Robot Localization and Map Building. A Multisensor Fusion Approach. Kluwer, Boston (1999)

    Book  Google Scholar 

  17. Čížek, P., Faigl, J.: On localization and mapping with RGB-D sensor and hexapod walking robot in rough terrains. In: Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, Budapest, pp. 2273–2278 (2016)

    Google Scholar 

  18. Davison, A.J., Reid, I., Molton, N., Stasse, O.: MonoSLAM: real-time single camera SLAM. IEEE Trans. Pattern Anal. Mach. Intell. 29(6), 1052–1067 (2007)

    Article  Google Scholar 

  19. Dellaert, F.: Factor graphs and GTSAM: a hands-on introduction. Technical Report, Georgia Institute of Technology (2012)

    Google Scholar 

  20. Dryanovski, I., Valenti, R., Xiao, J.: Fast visual odometry and mapping from RGB-D data. In: IEEE International Conference on Robotics and Automation, Karlsruhe, pp. 5704–5711 (2013)

    Google Scholar 

  21. Durrant-Whyte, H., Bailey, T.: Simultaneous localization and mapping: Part I. IEEE Robot. Autom. Mag. 13(2), 99–110 (2006)

    Article  Google Scholar 

  22. Eggert, D.W., Lorusso, A., Fisher, R.B.: Estimating 3-D rigid body transformations: a comparison of four major algorithms. Mach. Vis. Appl. 9(5–6), 272–290 (1997)

    Article  Google Scholar 

  23. Engel, J., Schöps, T., Cremers, D.: LSD-SLAM: large-scale direct monocular SLAM. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8690, pp. 834–849. Springer, Heidelberg (2014). doi:10.1007/978-3-319-10605-2_54

    Google Scholar 

  24. 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)

    Article  Google Scholar 

  25. Fischer, T., Pire, T., Čížek, P., De Cristóforis, P., Faigl, J.: Stereo vision-based localization for hexapod walking robots operating in rough terrains. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, Daejeon, pp. 2492–2497 (2016)

    Google Scholar 

  26. Forster, C., Pizzoli, M., Scaramuzza, D.: SVO: Fast semi-direct monocular visual odometry. In: IEEE International Conference on Robotics and Automation (2014)

    Google Scholar 

  27. Fraundorfer, F., Scaramuzza, D.: Visual odometry: part II - matching, robustness and applications. IEEE Robot. Autom. Mag. 19(2), 78–90 (2012)

    Article  Google Scholar 

  28. Fularz, M., Nowicki, M., Skrzypczyński, P.: Adopting feature-based visual odometry for resource-constrained mobile devices. In: Campilho, A., Kamel, M. (eds.) ICIAR 2014. LNCS, vol. 8815, pp. 431–441. Springer, Cham (2014). doi:10.1007/978-3-319-11755-3_48

    Google Scholar 

  29. Grisetti, G., Kümmerle, R., Stachniss, C., Burgard, W.: A tutorial on graph-based SLAM. IEEE Intell. Transp. Syst. Mag. 2(4), 31–43 (2010)

    Article  Google Scholar 

  30. Handa, A., Whelan, T., McDonald, J., Davison, A.: A benchmark for RGB-D visual odometry, 3D reconstruction and SLAM. In: IEEE International Conference on Robotics & Automation, Hong Kong (2014)

    Google Scholar 

  31. Hornung, A., Wurm, K., Bennewitz, M., Stachniss, C., Burgard, W.: OctoMap: an efficient probabilistic 3D mapping framework based on octrees. Auton. Robots 34(3), 189–206 (2013)

    Article  Google Scholar 

  32. Kaess, M.: Simultaneous localization and mapping with infinite planes. In: IEEE International Conference on Robotics and Automation, Seattle, pp. 4605–4611 (2015)

    Google Scholar 

  33. Kerl, C., Sturm, J., Cremers, D.: Robust odometry estimation for RGB-D cameras. In: Proceedings of the IEEE International Conference on Robotics & Automation, pp. 3748–3754 (2013)

    Google Scholar 

  34. Klein, G., Murray, D.: Parallel tracking and mapping for small AR workspaces. In: Proceedings of the International Symposium on Mixed and Augmented Reality, Nara, pp. 225–234 (2007)

    Google Scholar 

  35. Kraft, M., Nowicki, M., Penne, R., Schmidt, A., Skrzypczyński, P.: Efficient RGB-D data processing for feature-based self-localization of mobile robots. Int. J. Appl. Math. Comput. Sci. 26(1), 63–79 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  36. Kraft, M., Nowicki, M., Schmidt, A., Fularz, M., Skrzypczyński, P.: Toward evaluation of visual navigation algorithms on RGB-D data from the first- and second-generation Kinect. Machine Vision and Applications (2016)

    Google Scholar 

  37. Kümmerle, R., Grisetti, G., Strasdat, H., Konolige, K., Burgard, W.: g2o: A general framework for graph optimization. IEEE International Conference on Robotics & Automation, Shanghai, pp. 3607–3613 (2011)

    Google Scholar 

  38. Leonard, J.J., Durrant-Whyte, H.F.: Mobile robot localization by tracking geometric beacons. IEEE Trans. Robot. Autom. 7(3), 376–382 (1991)

    Article  Google Scholar 

  39. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)

    Article  Google Scholar 

  40. Lowry, S., Snderhauf, N., Newman, P., Leonard, J.J., Cox, D., Corke, P., Milford, M.J.: Visual place recognition: a survey. IEEE Trans. Robot. 32(1), 1–19 (2016)

    Article  Google Scholar 

  41. Lu, F., Milios, E.: Globally consistent range scan alignment for environment mapping. Auton. Robots 4, 333–349 (1997)

    Article  Google Scholar 

  42. 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)

    Article  Google Scholar 

  43. Mur-Artal, R., Tardós, J.D.: ORB-SLAM2: An open-source SLAM system for monocular, stereo and RGB-D cameras, arXiv preprint arxiv:1610.06475v1 (2016)

  44. Mur-Artal, R., Tardós, J.D.: Visual-inertial monocular SLAM with map reuse, arXiv preprint arxiv:1610.05949v1 (2016)

  45. Newcombe, R.A., Davison, A.J., Izadi, S., Kohli, P., Hilliges, O., Shotton, J., Molyneaux, D., Hodges, S., Kim, D., Fitzgibbon, A.: KinectFusion: real-time dense surface mapping and tracking. IEEE International Symposium on Mixed and Augmented Reality, Basel (2011)

    Google Scholar 

  46. Nowicki, M., Skrzypczyński, P.: Experimental verification of a walking robot self-localization system with the Kinect sensor. J. Autom. Mob. Robot. Intell. Syst. 7(4), 42–51 (2013)

    Google Scholar 

  47. Nowicki, M., Skrzypczyński, P.: Combining photometric and depth data for lightweight and robust visual odometry. European Conference on Mobile Robots, Barcelona, pp. 125–130 (2013)

    Google Scholar 

  48. Nüchter, A., Lingemann, K., Hertzberg, J., Surmann, H.: 6D SLAM - 3D mapping outdoor environments. J. Field Robot. 24(8–9), 699–722 (2007)

    Article  MATH  Google Scholar 

  49. Nüchter, A., Feyzabadi, S., Qiu, D., May, S.: SLAM à la carte - GPGPU for globally consistent scan matching. In: Proceedings of the 5th European Conference on Mobile Robots (ECMR), Örebro, pp. 271–276 (2011)

    Google Scholar 

  50. Park, J.-H., Shin, Y.-D., Bae, J.-H., Baeg, M.-H.: Spatial uncertainty model for visual features using a Kinect sensor. Sensors 12, 8640–8662 (2012)

    Article  Google Scholar 

  51. Paz, L., Piniés, P., Tardós, J.D., Neira, J.: Large-scale 6-DOF SLAM with stereo in hand. IEEE Trans. Robot. 24(5), 946–957 (2008)

    Article  Google Scholar 

  52. Piniés, P., Tardós, J.D.: Large-scale SLAM building conditionally independent local maps: application to monocular vision. IEEE Trans. Robot. 24(5), 1094–1106 (2008)

    Article  Google Scholar 

  53. Pire, T., Fischer, T., Civera, J., De Cristóforis, P., Berlles, J.J.: Stereo parallel tracking and mapping for robot localization. IEEE/RSJ International Conference on Intelligent Robots and Systems, Hamburg, Germany, pp. 1373–1378 (2015)

    Google Scholar 

  54. Rublee, E., Rabaud, V., Konolige, K., Bradski, G.: ORB: an efficient alternative to SIFT or SURF. In: IEEE International Conference on Computer Vision, pp. 2564–2571 (2011)

    Google Scholar 

  55. Rusu, R.B., Blodow, N., Marton, Z., Beetz, M.: Aligning point cloud views using persistent feature histograms. In: Proc. IEEE/RSJ International Conference on Intelligent Robots and Systems, Nice, pp. 3384–3391 (2008)

    Google Scholar 

  56. Rusu, R.B., Cousins, S.: 3D is here: Point Cloud Library (PCL). In: Proceedings of the IEEE International Conference on Robotics and Automation, Shanghai, pp. 1–4 (2011)

    Google Scholar 

  57. Scaramuzza, D., Fraundorfer, F.: Visual odometry: part I the first 30 years and fundamentals. IEEE Robot. Autom. Mag. 18(4), 80–92 (2011)

    Article  Google Scholar 

  58. Scherer, S., Zell, A.: (2013) Efficient onboard RGBD-SLAM for autonomous MAVs. In: Proceedings IEEE/RSJ International Conference on Intelligent Robots and Systems, Tokyo, Japan, pp. 1062–1068 (2013)

    Google Scholar 

  59. Schmidt, A., Kasiński, A.: The visual SLAM system for a hexapod robot. In: Bolc, L., Tadeusiewicz, R., Chmielewski, L.J., Wojciechowski, K. (eds.) ICCVG 2010. LNCS, vol. 6375, pp. 260–267. Springer, Heidelberg (2010). doi:10.1007/978-3-642-15907-7_32

    Chapter  Google Scholar 

  60. Schmidt, A.: The generalized, multi-robot framework for an augmented visual simultaneous localization and mapping system, Ph.D. Dissertation, Poznan University of Technology, Poznań (2014)

    Google Scholar 

  61. Schmidt, A., Kraft, M., Fularz, M., Domagala, Z.: Comparative assessment of point feature detectors and descriptors in the context of robot navigation. J. Autom. Mob. Robot. Intell. Syst. 7(1), 11–20 (2013)

    Google Scholar 

  62. Segal, A., Haehnel, D., Thrun, S.: Generalized-ICP. In: Proceedings of Robotics: Science and Systems, Seattle (2009)

    Google Scholar 

  63. Skrzypczyński, P.: Simultaneous localization and mapping: a feature-based probabilistic approach. Int. J. Appl. Math. Comput. Sci. 19(4), 575–588 (2009)

    MATH  Google Scholar 

  64. Skrzypczyński, P.: Laser scan matching for self-localization of a walking robot in man-made environments. Industr. Robot Int. J. 39(3), 242–250 (2012)

    Article  Google Scholar 

  65. Smith, R., Self, M., Cheeseman, P.: Estimating uncertain spatial relationships in robotics. In: Cox, I., Wilfong, G. (eds.) Autonomous Robot Vehicles, pp. 167–193. Springer, New York (1990)

    Chapter  Google Scholar 

  66. Steder, B., Rusu, R.B., Konolige, K., Burgard, W.: Point feature extraction on 3D range scans taking into account object boundaries. In: Proceedings of the IEEE International Conference on Robotics and Automation, Shanghai, pp. 2601–2608 (2011)

    Google Scholar 

  67. Stoyanov, T., Louloudi, A., Andreasson, H., Lilienthal, A.: Comparative evaluation of range sensor accuracy in indoor environments. In: Proceedings of the 5th European Conference on Mobile Robots (ECMR), Örebro, pp. 19–24 (2011)

    Google Scholar 

  68. Strasdat, H., Montiel, J.M.M., Davison, A.J.: Visual SLAM: why filter? Image Vis. Comput. 30(2), 65–77 (2012)

    Article  Google Scholar 

  69. Strasdat, H.: Local accuracy and global consistency for efficient visual SLAM. Ph.D. Dissertation, Imperial College, London (2012)

    Google Scholar 

  70. Sturm, J., Engelhard, N., Endres, F., Burgard, W., Cremers, D.: A benchmark for the evaluation of RGB-D SLAM systems. In: IEEE/RSJ International Conference on Intelligent Robots & Systems, Vilamoura, pp. 573–580 (2012)

    Google Scholar 

  71. Sünderhauf, M., Pham, T.T., Latif, Y., Milford, M., Reid, I.: Meaningful maps - object-oriented semantic mapping, arXiv preprint (2016)

    Google Scholar 

  72. Taguchi, Y., Jian, Y.D., Ramalingam, S., Feng, C.: Point-plane SLAM for hand-held 3D sensors, pp. 5182–5189. In: IEEE International Conference on Robotics & Automation, Karlsruhe (2013)

    Google Scholar 

  73. Triggs, B., McLauchlan, P.F., Hartley, R.I., Fitzgibbon, A.W.: Bundle adjustment — a modern synthesis. In: Triggs, B., Zisserman, A., Szeliski, R. (eds.) IWVA 1999. LNCS, vol. 1883, pp. 298–372. Springer, Heidelberg (2000). doi:10.1007/3-540-44480-7_21

    Chapter  Google Scholar 

  74. Tuytelaars, T., Mikolajczyk, K.: Local invariant feature detectors: a survey. Found. Trends Comput. Graph. Vis. 3(3), 177–280 (2008)

    Article  Google Scholar 

  75. Vysotska, O., Stachniss, C.: Exploiting building information from publicly available maps in graph-based SLAM. IEEE/RSJ International Conference on Intelligent Robots & Systems, Daejeon, pp. 4511–4516 (2016)

    Google Scholar 

  76. Weingarten, J., Siegwart, R.: 3D SLAM using planar segments. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3062–3067 (2006)

    Google Scholar 

  77. Whelan, T., Kaess, M., Johannsson, H., Fallon, M., Leonard, J.J., McDonald, J.: Real-time large-scale dense RGB-D SLAM with volumetric fusion. Int. J. Robot. Res. 34(4–5), 598–626 (2015)

    Article  Google Scholar 

  78. Wietrzykowski, J.: On the representation of planes for efficient graph-based SLAM with high-level features. J. Autom. Mob. Robot. Intell. Syst. 10(3), 3–11 (2016)

    Google Scholar 

Download references

Acknowledgements

This study has been supported by the National Science Centre, Poland under grant 2013/09/B/ST7/01583. The author would like to thank his colleagues: Dominik Belter, Michal Nowicki and Adam Schmidt for providing some material for illustrations.

Author information

Authors and Affiliations

  1. Institute of Control and Information Engineering, Poznań University of Technology, ul. Piotrowo 3A, 60-965, Poznań, Poland

    Piotr Skrzypczyński

Authors
  1. Piotr Skrzypczyński
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Piotr Skrzypczyński .

Editor information

Editors and Affiliations

  1. Industrial Research Institute for Automation and Measurements PIAP, Warsaw, Poland

    Roman Szewczyk

  2. Industrial Research Institute for Automation and Measurements PIAP, Warsaw, Poland

    Cezary Zieliński

  3. Industrial Research Institute for Automation and Measurements PIAP, Warsaw, Poland

    Małgorzata Kaliczyńska

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Skrzypczyński, P. (2017). Mobile Robot Localization: Where We Are and What Are the Challenges?. In: Szewczyk, R., Zieliński, C., Kaliczyńska, M. (eds) Automation 2017. ICA 2017. Advances in Intelligent Systems and Computing, vol 550. Springer, Cham. https://doi.org/10.1007/978-3-319-54042-9_23

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-54042-9_23

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-54041-2

  • Online ISBN: 978-3-319-54042-9

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation