Abstract
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.
Chapter PDF
Similar content being viewed by others
References
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)
Smith, R.C., Cheeseman, P.: On the representation and estimation of spatial uncertainty. Technical Report TR 4760 & 7239, SRI (1985)
Durrant-Whyte, H., Bailey, T.: Simultaneous localization and mapping: part I. IEEE Robotics & Automation Magazine 13(2), 99–110 (2006)
Bailey, T., Durrant-Whyte, H.: Simultaneous localization and mapping: part II. IEEE Robotics & Automation Magazine 13(3), 108–117 (2006)
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)
Civera, J., Davison, A.J., Montiel, J.: Inverse Depth Parametrization for MonocularSLAM. IEEE Trans. on Robotics 24(5), 932–945 (2008)
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)
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)
Dijkstra, E.W.: A note on two problems in connection with graphs. Numerische Mathematik 1, 269–271 (1959)
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)
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)
Zilberstein, S.: Using Anytime Algorithms in Intelligent Systems. AI Magazine (Fall 1996)
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)
Overmars, M.: A random approach to motion planning. Tech. rep., Utrecht University (October 1992)
LaValle, S.M.: Rapidly-Exploring Random Trees: A New Tool for Path Planning. Tech. Rep. 98-11, Iowa State University, Ames, IA (October 1998)
Ferguson, D., Stentz, A.: Anytime RRTs. In: Proceedings of the IEEE International Conference on Intelligent Robots and Systems, IROS (2006)
Ferguson, D., Stentz, A.: Anytime, Dynamic Planning in High-dimensional Search Spaces. In: Proceedings of the IEEE International Conference on Robotics and Automation (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lin, SY., Chen, YC. (2011). SLAM and Navigation in Indoor Environments. In: Ho, YS. (eds) Advances in Image and Video Technology. PSIVT 2011. Lecture Notes in Computer Science, vol 7087. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25367-6_5
Download citation
DOI: https://doi.org/10.1007/978-3-642-25367-6_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25366-9
Online ISBN: 978-3-642-25367-6
eBook Packages: Computer ScienceComputer Science (R0)