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In this paper, we describe a concurrent map-building and localization (CML) system based on a multi-hypotheses tracker that is able to build and refine autonomously the appearance map required for localization as the robot moves in the environment.
They are designed to model only the variabilities of small-scale fading conditioned on a fixed large-scale fading condition because when a significant object moves in the environment and changes the large-scale fading condition, the methods usually need to be retrained for maintaining the performance.
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To this end a crucial component is to avoid interferences when moving in the environment.
There are one to seven targets which are moving in the environment.
Using a car, the Rx is moved in the environment with a constant speed of 0.8 m/s.
They move in the environment (randomly or using a more complex strategy) using their sensors to locate objects.
The first algorithm builds a map in the form of a collection of scans corresponding to a subset of the poses of a robot moving in the environment.
The higher scores also meant that the Turtlebot would keep moving in the environment for a longer time and avoid more obstacles.
The agents could move in the environment freely and their neural activity was assessed.
The agents could move in the environment freely as their neural activity was measured.
In particular, people expectations from daily life regarding how human beings move in the environment differ considerably from expectations regarding the motion of inanimate objects [19].
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