Slam techniques with probabilistic filters; characterization and results in mobile robots
DOI:
https://doi.org/10.61799/2216-0388.408Keywords:
Kalman filter, localization, mobile robot, tsensor, slamAbstract
he challenge of locating and mapping SLAM in mobile robots is to discover if it is possible to navigate through an unknown environment and incrementally build a consistent map of it, while the robot at the same time determining its position within the map. The theoretical-conceptual solution has been developed by the SLAM especially with location filters, such as: the Kalman filter, the Information filter, the graphic filter and the particle filter among others. The research carried out by varying established marks in the trajectories and analyzing these filters from the probabilistic point of view, these filters are incorporated in two prototypes of mobile robots, which have ultrasound and laser sensors. The article shows the results of errors in odometry and necessary times that each SLAM filter has on average per iteration for the construction of the two-dimensional map.
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