Skip to main content

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 215))

  • 3323 Accesses


This paper presents an implementation of indoor Simultaneous Localization and Mapping (SLAM) using RGBD images. Such system can be used in applications such as indoor robot navigation and environment perception. We perform coarse frame alignments using visual features. The coarse alignment results are then refined by applying Iterative Closest Point (ICP) algorithm to the point clouds. We create a pose graph which consists of keyframes which will be optimized if a new loop is detected. The performances of coarse alignment are tested using four methods—KLT tracker, SIFT, SURF and ORB. The experiment results show that ORB is a good trade-off between accuracy and efficiency. The performances and limitations of ICP are also explored. The results indicate that ICP is very sensitive to the initial value and the size of the point clouds. We also find that the loop closing largely reduces the alignment error. The maps of our laboratory are created using both the 3D point clouds and octomap.

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

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
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

Similar content being viewed by others


  1. Montemerlo M, Thrun S, Koller D, Wegbreit B (2003) Fastslam 2.0: an improved particle filtering algorithm for simultaneous localization and mapping that provably converges. In: International joint conference on artificial intelligence, vol 18. Citeseer, pp 1151–1156

    Google Scholar 

  2. Thrun S, Montemerlo M (2006) The graph slam algorithm with applications to large-scale mapping of urban structures. Int J Robot Res 25(5–6):403–429

    Article  Google Scholar 

  3. Konolige K, Grisetti G, Kummerle R, Burgard W, Limketkai B, Vincent, R (2010) Efficient sparse pose adjustment for 2d mapping. In: Intelligent robots and systems (IROS), (2010) IEEE/RSJ international conference on, IEEE, pp 22–29

    Google Scholar 

  4. Klein G, Murray D (2007) Parallel tracking and mapping for small ar workspaces. In: Proceedings of the (2007) 6th IEEE and ACM international symposium on mixed and augmented reality. IEEE Computer Society, pp 1–10

    Google Scholar 

  5. Endres F, Hess J, Engelhard N, Sturm J, Cremers D, Burgard W (2012) An evaluation of the rgb-d slam system

    Google Scholar 

  6. des Bouvrie B (2011) Improving rgbd indoor mapping with imu data

    Google Scholar 

  7. Herrera CD, Kannala J, Heikkilä J (2011) Accurate and practical calibration of a depth and color camera pair. In: Computer analysis of images and patterns, Springer, pp 437–445

    Google Scholar 

  8. Lowe D (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vision 60(2):91–110

    Article  Google Scholar 

  9. Bay H, Tuytelaars T, Van Gool L (2006) Surf: Speeded up robust features. Comput Vis ECCV 2006 1:404–417

    Google Scholar 

  10. Wu C (2007) SiftGPU: A GPU implementation of scale invariant feature transform (SIFT). ccwu/siftgpu

  11. Hertzberg C, Wagner R, Birbach O, Hammer T, Frese, U.: Experiences in building a visual slam system from open source components. In: Robotics and automation (ICRA), (2011) IEEE international conference on, IEEE, pp 2644–2651

    Google Scholar 

  12. Calonder M, Lepetit V, Strecha C, Fua P (2010) Brief: Binary robust independent elementary features. Comput Vis ECCV 2010 6314:778–792

    Google Scholar 

  13. Rublee E, Rabaud V, Konolige K, Bradski G (2011) Orb: an efficient alternative to sift or surf. In: International conference on computer vision, Barcelona, 2011

    Google Scholar 

  14. Neumann D, Lugauer F, Bauer S, Wasza J, Hornegger, J.: Real-time rgb-d mapping and 3-d modeling on the gpu using the random ball cover data structure. In: Computer vision workshops (ICCV Workshops), (2011) IEEE international conference on, IEEE, pp 1161–1167

    Google Scholar 

  15. Tomasi C, Shi J (1994) Good features to track. CVPR94, pp 593–600

    Google Scholar 

  16. Blanco JL (2010) A tutorial on se(3) transformation parameterizations and on-manifold optimization. Technical report, University of Malaga

    Google Scholar 

  17. Segal A, Haehnel D, Thrun S (2009) Generalized-icp. In: Proceedings of robotics: science and systems (RSS)

    Google Scholar 

  18. Rusu RB, Cousins S (2011) 3D is here: point cloud library (PCL). In: IEEE international conference on robotics and automation (ICRA), Shanghai, China, 9–13 May 2011

    Google Scholar 

  19. Stachniss C, Kretzschmar H (2011) Pose graph compression for laser-based slam

    Google Scholar 

  20. Konolige K, Agrawal M (2008) Frameslam: from bundle adjustment to real-time visual mapping. IEEE Trans Rob 24(5):1066–1077

    Article  Google Scholar 

  21. Sivic J, Zisserman A (2003) Video google: a text retrieval approach to object matching in videos. In: Computer Vision, Proceedings. Ninth IEEE International Conference on, IEEE, pp 1470–1477

    Google Scholar 

  22. Cummins M, Newman P (2008) Fab-map: probabilistic localization and mapping in the space of appearance. Int J Robot Res 27(6):647–665

    Article  Google Scholar 

  23. Wurm KM, Hornung A, Bennewitz M, Stachniss C, Burgard W (2010) OctoMap: a probabilistic, flexible, and compact 3D map representation for robotic systems. In: Proceedings of the ICRA 2010 workshop on best practice in 3D perception and modeling for mobile manipulation, Anchorage, AK, USA, May 2010.

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to Rongyi Lin .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Lin, R., Wang, Y., Yang, S. (2014). RGBD SLAM for Indoor Environment. In: Sun, F., Hu, D., Liu, H. (eds) Foundations and Practical Applications of Cognitive Systems and Information Processing. Advances in Intelligent Systems and Computing, vol 215. Springer, Berlin, Heidelberg.

Download citation

  • DOI:

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-37834-8

  • Online ISBN: 978-3-642-37835-5

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics