Multirobot Tethering
Solving the Localization Problem
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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 confidence pose estimates under these conditions, more robust localization strategies are required to produce reliable pose estimates. In order to eliminate the low confidence estimates produced in certain environments, a multirobot system is designed to introduce mobile environment features. Tracking a...
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 confidence pose estimates under these conditions, more robust localization strategies are required to produce reliable pose estimates. In order to eliminate the low confidence estimates produced in certain environments, a multirobot system is designed to introduce mobile environment features. Tracking and controlling a secondary robot introduces a known feature in the environment which can ensure a high confidence estimate. From this knowledge, an autonomous robot can confidently navigate in even the most difficult (featureless) environments.
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