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Collaborative Exploration Based on Simultaneous Localization and Mapping

Collaborative Exploration Based on Simultaneous Localization and Mapping

Domenec Puig
Copyright: © 2013 |Pages: 30
ISBN13: 9781466626720|ISBN10: 1466626720|EISBN13: 9781466627031
DOI: 10.4018/978-1-4666-2672-0.ch017
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MLA

Puig, Domenec. "Collaborative Exploration Based on Simultaneous Localization and Mapping." Robotic Vision: Technologies for Machine Learning and Vision Applications, edited by Jose Garcia-Rodriguez and Miguel A. Cazorla Quevedo, IGI Global, 2013, pp. 303-332. https://doi.org/10.4018/978-1-4666-2672-0.ch017

APA

Puig, D. (2013). Collaborative Exploration Based on Simultaneous Localization and Mapping. In J. Garcia-Rodriguez & M. Cazorla Quevedo (Eds.), Robotic Vision: Technologies for Machine Learning and Vision Applications (pp. 303-332). IGI Global. https://doi.org/10.4018/978-1-4666-2672-0.ch017

Chicago

Puig, Domenec. "Collaborative Exploration Based on Simultaneous Localization and Mapping." In Robotic Vision: Technologies for Machine Learning and Vision Applications, edited by Jose Garcia-Rodriguez and Miguel A. Cazorla Quevedo, 303-332. Hershey, PA: IGI Global, 2013. https://doi.org/10.4018/978-1-4666-2672-0.ch017

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Abstract

This chapter focuses on the study of SLAM taking into account different strategies for modeling unknown environments, with the goal of comparing several methodologies and test them in real robots even if they are heterogeneous. The purpose is to combine them in order to reduce the exploration time. Indubitably, it is not an easy work because it is important to take into account the problem of integrating the information related with the changes into the map. In this way, it is necessary to obtain a representation of the surrounding in an efficiently way. Furthermore, the author is interested in the collaboration between robots, because it is well-known that a team of robots is capable of completing a given task faster than a single robot. This assumption will be checked by using both simulations and real robots in different experiments. In addition, the author combines the benefits of both vision-based and laser-based systems in the integration of the algorithms.

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