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- W119622068 abstract "HE indoor localization problem consists on estimating the robot’s location using absolute (reference-based systems) and relative (also called dead-reckoning) position measurements [3], which give the situation of the environment around the robot and the feedback about its driving actions respectively. Basically, it consists on answering the question Where am I? [16] from the robot’s point of view given: a representation of the world where the robot is navigating and sensor information. Localization methods are usually distinguished according to the type of problem they tackle [13]; in particular, i) tracking or local techniques, and ii) global techniques. Commonly, local techniques present advantages in accuracy and efficiency, while global techniques are much more robust [14]. What makes this problem difficult is the presence of uncertainty in the measurements because it usually results hard to represent noisy measures. These measures are usually stated by giving a range of values that are likely to enclose the real value. From the probabilistic point of view, this range is usually designated by repeating the measure under the same conditions enough times for modeling the uncertainty using a probabilistic function. In other words, probabilistic functions should be experimentally calculated which implies reproducing the measure under the same conditions. However, sometimes it is not possible to repeat the measure or reproduce the factors that influence this. Techniques based on fuzzy logic only require an approximate sensor model [6] of the measures. Moreover, position measurements may be affected by different types" @default.
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- W119622068 date "2007-01-01" @default.
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- W119622068 title "Indoor fuzzy self-localization using fuzzy segments" @default.
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- W119622068 doi "https://doi.org/10.14198/jopha.2007.1.1.06" @default.
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