SLAM and Navigation in Indoor Environments

  • Shang-Yen Lin
  • Yung-Chang Chen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7087)


In this paper, we propose a system for wheeled robot SLAM and navigation in indoor environments. An omni-directional camera and a laser range finder are the sensors to extract the point features and the line features as the landmarks. In SLAM and self-localization while navigation, we use extended Kalman filter (EKF) to deal with the uncertainty of robot pose and landmark feature estimation. After the map is built, robot can navigate in the environment based on it. We apply two scale path-planning for navigation. The large-scale planning finds an appropriate path from starting point to destination. The local-scale path-planning fills up the drawbacks of the prior step, such as dealing with the static and dynamic obstacles and smoothing the path for easier robot following. Through the experiment results, we show that the proposed system can smoothly and correctly locate itself, build the environment map and navigate in indoor environments.


SLAM EKF navigation path-planning obstacle avoidance robot 


  1. 1.
    Smith, R., Self, M., Cheeseman, P.: Estimating uncertain spatial relationships in robotics. In: Cox, I.J., Wilfong, G.T. (eds.) Autonomous Robot Vehicles, pp. 167–193. Springer, Heidelberg (1990)CrossRefGoogle Scholar
  2. 2.
    Smith, R.C., Cheeseman, P.: On the representation and estimation of spatial uncertainty. Technical Report TR 4760 & 7239, SRI (1985)Google Scholar
  3. 3.
    Durrant-Whyte, H., Bailey, T.: Simultaneous localization and mapping: part I. IEEE Robotics & Automation Magazine 13(2), 99–110 (2006)CrossRefGoogle Scholar
  4. 4.
    Bailey, T., Durrant-Whyte, H.: Simultaneous localization and mapping: part II. IEEE Robotics & Automation Magazine 13(3), 108–117 (2006)CrossRefGoogle Scholar
  5. 5.
    Doucet, A., de Freitas, J.F.G., Murphy, K., Russel, S.: Rao-Blackwellized particle filtering for dynamic Bayesian networks. In: Proc. of the Conf. on Uncertainty in Artificial Intelligence (UAI), Stanford, CA, USA, pp. 176–183 (2000)Google Scholar
  6. 6.
    Civera, J., Davison, A.J., Montiel, J.: Inverse Depth Parametrization for MonocularSLAM. IEEE Trans. on Robotics 24(5), 932–945 (2008)CrossRefGoogle Scholar
  7. 7.
    Chang, H.H., Lin, S.Y., Chen, Y.C.: SLAM for Indoor Environment Using Stereo Vision. In: Second WRI Global Congress on Intelligent Systems (2010)Google Scholar
  8. 8.
    Kuo, B.W., Chang, H.H., Lin, S.Y., Chen, Y.C., Huang, S.Y.: A Light-and-Fast SLAM Algorithm for Robots in Indoor Environments using Line Segment Map. Journal of Robotics (2011)Google Scholar
  9. 9.
    Dijkstra, E.W.: A note on two problems in connection with graphs. Numerische Mathematik 1, 269–271 (1959)MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Hart, P., Nilsson, N., Raphael, B.: A formal basis for the heuristic determination of minimum cost paths. IEEE Transactions on Systems Science and Cybernetics 4(2), 100–107 (1968)CrossRefGoogle Scholar
  11. 11.
    Stentz: Optimal and Efficient Path Planning for Partially-Known Environments. In: Proceedings of 1994 IEEE International Conference on Robotics and Automation, vol. 4, pp. 3310–3317 (May 1994)Google Scholar
  12. 12.
    Zilberstein, S.: Using Anytime Algorithms in Intelligent Systems. AI Magazine (Fall 1996)Google Scholar
  13. 13.
    Likhachev, M., Ferguson, D., Gordon, G., Stentz, A., Thrun, S.: Anytime Dynamic A*: An Anytime, Replanning Algorithm. In: International Conference on Automated Planning & Scheduling (2005)Google Scholar
  14. 14.
    Overmars, M.: A random approach to motion planning. Tech. rep., Utrecht University (October 1992)Google Scholar
  15. 15.
    LaValle, S.M.: Rapidly-Exploring Random Trees: A New Tool for Path Planning. Tech. Rep. 98-11, Iowa State University, Ames, IA (October 1998)Google Scholar
  16. 16.
    Ferguson, D., Stentz, A.: Anytime RRTs. In: Proceedings of the IEEE International Conference on Intelligent Robots and Systems, IROS (2006)Google Scholar
  17. 17.
    Ferguson, D., Stentz, A.: Anytime, Dynamic Planning in High-dimensional Search Spaces. In: Proceedings of the IEEE International Conference on Robotics and Automation (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Shang-Yen Lin
    • 1
  • Yung-Chang Chen
    • 1
  1. 1.Department of Electrical EngineeringNational Tsing Hua UniversityHsinchuTaiwan

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