Abstract:Solving the SLAM (Simultaneous Localization And Mapping) problem increases the robot autonomy allowing the robot to build a map of the environment and simultaneously, estimate its pose (position and orientation) using this map. Usually, the robot pose is computed by means of odometry. This method uses the sensor information, nevertheless, these sensors introduce noise on the measurements, generating uncertainty in robot localization. For this reason, probabilistic methods as Kalman and Particle filters have be… Show more
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