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Enhanced PCA-Based Localization Using Depth Maps with Missing Data

Experimental Validation

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Abstract

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.

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Authors and Affiliations

  1. IDMEC/LAETA - Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais 1, 1049-001, Lisboa, Portugal

    Fernando Carreira, João M. F. Calado, Carlos Cardeira & Paulo Oliveira

  2. ADEM - Instituto Superior de Engenharia de Lisboa, Instituto Politécnico de Lisboa, R. Conselheiro Emídio Navarro, 1, 1959-007, Lisboa, Portugal

    Fernando Carreira & João M. F. Calado

  3. ISR/LARSyS - Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais 1, 1049-001, Lisboa, Portugal

    Paulo Oliveira

Authors
  1. Fernando Carreira
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  2. João M. F. Calado
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  3. Carlos Cardeira
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  4. Paulo Oliveira
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Correspondence to Fernando Carreira.

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Carreira, F., Calado, J.M.F., Cardeira, C. et al. Enhanced PCA-Based Localization Using Depth Maps with Missing Data. J Intell Robot Syst 77, 341–360 (2015). https://doi.org/10.1007/s10846-013-0013-6

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  • DOI: https://doi.org/10.1007/s10846-013-0013-6

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