Abstract
Mapping, localization and navigation are major topics and challenges for mobile robotics. To perform tasks and to interact efficiently in the environment, a robot needs knowledge about its surroundings. Many robots today are capable of performing simultaneous mapping and localization to generate own world representations. Most assume an array of highly sophisticated artificial sensors to track landmarks placed in the environment. Recently, there has been significant interest in research approaches inspired by nature and RatSLAM is one of them. It has been introduced and tested on wheeled robots with good results. To examine how RatSLAM behaves on humanoid robots, we adapt this model for the first time to this platform by adjusting the given constraints. Furthermore, we introduce a multiple hypotheses mapping technique which improves mapping robustness in open spaces with features visible from several distant locations.
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Müller, S., Weber, C., Wermter, S. (2014). RatSLAM on Humanoids - A Bio-Inspired SLAM Model Adapted to a Humanoid Robot. In: Wermter, S., et al. Artificial Neural Networks and Machine Learning – ICANN 2014. ICANN 2014. Lecture Notes in Computer Science, vol 8681. Springer, Cham. https://doi.org/10.1007/978-3-319-11179-7_99
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DOI: https://doi.org/10.1007/978-3-319-11179-7_99
Publisher Name: Springer, Cham
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