Abstract
The contribution introduces design and comparison of different-brand methods for position localization of indoor mobile robots. The both methods derive the robot relative position from structure of the working environment based on range measurements gathered by a LIDAR system. As one of the methods uses statistical description of the scene the other relies on a feature-based matching approach. The suggested localization methods have been experimentally verified and the achieved results are presented and discussed.
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Mázl, R., Kulich, M., Přeučil, L. (2001). Statistical and Feature-Based Methods for Mobile Robot Position Localization. In: Mayr, H.C., Lazansky, J., Quirchmayr, G., Vogel, P. (eds) Database and Expert Systems Applications. DEXA 2001. Lecture Notes in Computer Science, vol 2113. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44759-8_51
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DOI: https://doi.org/10.1007/3-540-44759-8_51
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