Abstract
In the domain of mobile robots local maps of environments are used as knowledge base for decisions allowing reactive control in order to prevent collisions when following a global trajectory. These maps are normally discrete and updated at relatively high frequency, but with no dynamic information. The proposed framework uses a sparse description of clustered scan points from a laser range scanner. These features and the system odometry are used to predict the agent’s ego motion as well as feature motion using an Extended Kalman Filter. This approach is similar to the Simultaneous Localization and Map** (SLAM) algorithm but with low-constraint features. The presented local Simultaneous Localization and Map** (LSLAM) approach creates a decision base, holding a dynamic description which relaxes the requirement of high update rates. Simulated results demonstrate environment classification and tracking as well as self-pose correction in static and in dynamic environments.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
Merging uncertainty computed through Gaussian distributions multiplication.
- 2.
Evaluating the Mahalanobis distance ensures scalability and behaves equally for various pose uncertainties of the system.
References
Montemerlo, M., Thrun, S., Koller, D., Wegbreit, B.: FastSLAM: a factored solution to the simultaneous localization and map** problem. In: Proceedings of the AAAI national conference on artificial intelligence (2002)
Nüchter, A., Lingemann, K., Hertzberg, J., Surmann, H.: 6D SLAM—3D map** outdoor environments. J. Field Robot. 24, 699–722 (2007)
Rusu, R.B., Sucan, I.A., Gerkey, B., Chitta, S., Beetz, M., Kavraki, L.E.: Real-time perception-guided motion planning for a personal robot. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 4245–4252 (2009)
Choudhary, S., Trevor, A.J.B., Christensen, H.I., Dellaert, F.: SLAM with object discovery, modeling and map**. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1018–1025 (2014)
Montesano, L.: Detection and Tracking of Moving Objects from a Mobile Platform. Application to Navigation and Multi-robot Localization. University of Zaragoza, Zaragoza (2006)
Bar-Shalom, Y., Rong Li, X., Kirubarajan, T.: Estimation with applications to tracking and navigation. Wiley, New York (2001)
Hu, C., Chen, W., Chen, Y., Liu, D.: Adaptive Kalman filtering for vehicle navigation. J. Glob. Positioning Syst. 2(1), 42–47 (2003)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Todoran, H.G., Bader, M. (2016). Extended Kalman Filter (EKF)-Based Local SLAM in Dynamic Environments: A Framework. In: Borangiu, T. (eds) Advances in Robot Design and Intelligent Control. Advances in Intelligent Systems and Computing, vol 371. Springer, Cham. https://doi.org/10.1007/978-3-319-21290-6_46
Download citation
DOI: https://doi.org/10.1007/978-3-319-21290-6_46
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-21289-0
Online ISBN: 978-3-319-21290-6
eBook Packages: EngineeringEngineering (R0)