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Multirobot Tethering Solving Localization by Baillio Brad (5 results)
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Multirobot Tethering: Solving the Localization Problem
Language: English
Published by LAP LAMBERT Academic Publishing, 2013
ISBN 10: 3659461369 ISBN 13: 9783659461361
Condition: New.
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Multirobot Tethering | Solving the Localization Problem
Language: English
Published by LAP LAMBERT Academic Publishing, 2013
ISBN 10: 3659461369 ISBN 13: 9783659461361
Taschenbuch. Condition: Neu. Multirobot Tethering | Solving the Localization Problem | Brad Baillio | Taschenbuch | 64 S. | Englisch | 2013 | LAP LAMBERT Academic Publishing | EAN 9783659461361 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.
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Multirobot Tethering: Solving the Localization Problem
Language: English
Published by LAP LAMBERT Academic Publishing, 2013
ISBN 10: 3659461369 ISBN 13: 9783659461361
Condition: New. Print on Demand.
-
Multirobot Tethering: Solving the Localization Problem
Language: English
Published by LAP LAMBERT Academic Publishing, 2013
ISBN 10: 3659461369 ISBN 13: 9783659461361
Condition: New. PRINT ON DEMAND.
-
Multirobot Tethering : Solving the Localization Problem
Language: English
Published by LAP LAMBERT Academic Publishing, 2013
ISBN 10: 3659461369 ISBN 13: 9783659461361
Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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.
