NDLI: Hybridization of Monte Carlo and set-membership methods for the global localization of underwater robots

Content Provider	IEEE Xplore Digital Library
Author	Neuland, R. Nicola, J. Maffei, R. Jaulin, L. Prestes, E. Kolberg, M.
Copyright Year	2014
Description	Author affiliation: ENSTA Bretagne, Brest, France (Nicola, J.; Jaulin, L.) \|\| Inf. Inst., Fed. Univ. of Rio Grande do Sul, Porto Alegre, Brazil (Neuland, R.; Maffei, R.; Prestes, E.; Kolberg, M.)
Abstract	Probabilistic approaches are extensively used to solve high-dimensionality problems in many different fields. The particle filter is a prominent approach in the field of Robotics, due to its adaptability to non-linear models with multi-modal distributions. Nonetheless, its result is strongly dependent on the quality and the number of samples required to cover the space of possible solutions. In contrast, interval analysis deals with high-dimensionality problems by reducing the space enclosing the actual solution. Notwithstanding, it cannot precise where in the resulting subspace the actual solution is. We devised a strategy that combines the best of both worlds. Our approach is illustrated by solving the global localization problem for underwater robots.
Starting Page	199
Ending Page	204
File Size	402098
Page Count	6
File Format	PDF
e-ISBN	9781479969340
DOI	10.1109/IROS.2014.6942561
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-09-14
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Robot sensing systems Transponders Probabilistic logic Uncertainty Mathematical model Equations
Content Type	Text
Resource Type	Article

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