Concurrent map building and localization on indoor dynamic environments
A system that builds and maintains a dynamic map for a mobile robot is presented. A learning rule associated to each observed landmark is used to compute its robustness. The position of the robot during map construction is estimated by combining sensor readings, motion commands, and the current map...
| Autores: | , |
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| Tipo de documento: | artigo |
| Data de publicação: | 2002 |
| País: | España |
| Recursos: | Consejo Superior de Investigaciones Científicas (CSIC) |
| Repositório: | DIGITAL.CSIC. Repositorio Institucional del CSIC |
| OAI Identifier: | oai:digital.csic.es:10261/30533 |
| Acesso em linha: | http://hdl.handle.net/10261/30533 |
| Access Level: | Acceso aberto |
| Palavra-chave: | Mobile robot map learning Mobile robot navigation CML SLAM Extended Kalman filter Robots |
| Resumo: | A system that builds and maintains a dynamic map for a mobile robot is presented. A learning rule associated to each observed landmark is used to compute its robustness. The position of the robot during map construction is estimated by combining sensor readings, motion commands, and the current map state by means of an Extended Kalman Filter. The combination of landmark strength validation and Kalman filtering for map updating and robot position estimation allows for robust learning of moderately dynamic indoor environments. |
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