Carreira, FernandoCalado, João Manuel FerreiraCardeira, CarlosOliveira, Paulo2016-03-082016-03-082015-02CARREIRA, 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. 341-3600921-02961573-0409http://hdl.handle.net/10400.21/5809In 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.engMobile robotsRobot sensing systemsSensor fusionPrincipal component analysisKalman filtersjournal article10.1007/s10846-013-0013-6