Robust Mobile Robot Localization by Tracking Natural Landmarks
This article presents a feature-based localization framework to use with conventional 2D laser rangefinder. The system is based on the Unscented Kalman Filter (UKF) approach, which can reduce the errors in the calculation of the robot’s position and orientation. The framework consists of two main parts: feature extraction and multi-sensor fusing localization. The novelty of this system is that a new segmentation algorithm based-on the micro-tangent line (MTL) is introduced. Features, such as lines, corners and curves, can be characterized from the segments. For each landmark, the geometrical parameters are provided with statistical information, which are used in the subsequent matching phase, together with a priori map, so as to get an optimal estimate of the robot pose. Experimental results show that the proposed localization method is efficient in office-like environment.
KeywordsLocalization feature extraction Unscented Kalman Filter mobile robot
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- 1.Iyengar, S., Elfes, A.: Autonomous Mobile Robots, vol. 1, 2. IEEE Computer Society Press, Los Alamitos (1991)Google Scholar
- 2.Jensfelt, P., Christensen, H.: Laser Based Position Acquisition and Tracking in an Indoor Environment. In: IEEE Int. Proc. on Robotics and Automation, vol. 1 (1998)Google Scholar
- 3.Julier, S.J., Uhlmann, J.K., Durrant-Whyte, H.F.: A new approach for filtering nonlinear systems. In: Proc. Am. Contr. Conf., Seattle, WA, pp. 1628–1632 (1995)Google Scholar
- 5.Arras, K.O., Siegwart, R.: Feature extraction and scene interpretation for map based navigation and map building. In: Proc. of SPIE, Mobile Robotics XII, vol. 3210 (1997)Google Scholar
- 6.Nash, J.C.: Compact numerical methods for computers: linear algebra and function minimization. Adam Hilger Ltd. (1979)Google Scholar
- 7.Nunez, P., Vazquez-Martin, R., del Toro, J.C., Bandera, A., Sandoval, F.: A Curvature based Method to Extract Natural Landmarks for Mobile Robot Navigation. In: IEEE Int. Symposium on Intelligent Signal Processing 2007, October 2007, pp. 1–6 (2007)Google Scholar