Abstract
Recent research shows more and more interest in exploring the mapping task of tagged-items in the context of inventory with mobile robots in passive radio-frequency identification (RFID)-equipped infrastructures. However, mapping RFID tags is quite challenging, since the characteristics of radio signals are heavily influenced by environmental effects (e.g., reflection, diffraction, or absorption). This paper presents the augmented particle filter, which is able to recover from mapping failures of static RFID tags and localize non-static RFID tags. Furthermore, although negative information is usually considered to be less informative than positive information, we exploit the usefulness of negative information for RFID-based mapping. We show that a careful examination of negative information improves the mapping accuracy and helps to recover from mapping failures and relocalize non-static RFID tags. Additionally, we compare the particle filter-based approach to our previous grid-based Markov localization approach. Last, we demonstrate a mobile system, which is able to approach both static and non-static RFID tags, and avoid obstacles at the same time.
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Liu, R., Zell, A. (2016). Toward Localizing both Static and Non-static RFID Tags with a Mobile Robot. In: Menegatti, E., Michael, N., Berns, K., Yamaguchi, H. (eds) Intelligent Autonomous Systems 13. Advances in Intelligent Systems and Computing, vol 302. Springer, Cham. https://doi.org/10.1007/978-3-319-08338-4_21
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DOI: https://doi.org/10.1007/978-3-319-08338-4_21
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