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Advisor(s)
Abstract(s)
In this paper a new method for self-localization of mobile robots, based on a PCA positioning sensor to operate in unstructured environments, is proposed and experimentally validated. The proposed PCA extension is able to perform the eigenvectors computation from a set of signals corrupted by missing data. The sensor package considered in this work contains a 2D depth sensor pointed upwards to the ceiling, providing depth images with missing data. The positioning sensor obtained is then integrated in a Linear Parameter Varying mobile robot model to obtain a self-localization system, based on linear Kalman filters, with globally stable position error estimates. A study consisting in adding synthetic random corrupted data to the captured depth images revealed that this extended PCA technique is able to reconstruct the signals, with improved accuracy. The self-localization system obtained is assessed in unstructured environments and the methodologies are validated even in the case of varying illumination conditions.
Description
Keywords
Mobile robots Robot sensing systems Sensor fusion Principal component analysis Kalman filters
Citation
CARREIRA, Fernando; [et al.] - Enhanced PCA-based localization using depth maps with missing data. Journal of Intelligent & Robotics Systems. ISSN. 0921-0296. Vol. 77, N.º 2, SI (2015), pp. 341-360
Publisher
Springer