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Showing 1 to 4 of 4 for “"Monte Carlo Localization"”.
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Adapting Monte Carlo Localization to Utilize Floor and Wall Texture Data
<p>Monte Carlo Localization (MCL) is an algorithm that allows a robot to determine its location when provided a map of its surroundings. Particles, consisting of a location and an orientation, represent possible positions where the robot could be on the map. The probability of the robot being at …
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Topological mapping for limited sensing mobile robots using the Probabilistic Gap Navigation Tree
… to the particle filtering algorithm used in Monte Carlo localization. Therefore, the PGNT allows navigation in an unknown environment using a realistic range finder, as opposed to the ideal sensor model assumed previously. We demonstrate the ability to build a PGNT in a simulated environment.
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Multirobot Tethering for Localization and Control
Particle filtering has proven to be an effective localization method for wheeled autonomous vehicles. For a given map, a sensor model, and observations, occasions arise where the vehicle could equally likely be in many locations of the map. Because particle filtering algorithms may generate low …
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Multistage Localization for High Precision Mobile Manipulation Tasks
This paper will present a multistage localization approach for an autonomous industrial mobile manipulator (AIMM). This approach allows tasks with an operational scope outside the range of the robot's manipulator to be completed without having to recalibrate the position of the end-effector each …