An embedded particle filter SLAM implementation using an affordable platform
Resumen:
The recent growth in robotics applications has put to evidence the need for autonomous robots. In order for a robot to be truly autonomous, it must be able to solve the navigation problem. This paper highlights the main features of a fully embedded particle filter SLAM system and introduces some novel ways of calculating a measurement likelihood. A genetic algorithm calibration approach is used to prevent parameter over-fitting and obtain more generalizable results. Finally, it is depicted how the developed SLAM system was used to autonomously perform a field covering task showing robustness and better performance than a reference system. Several lines of possible improvements to the present system are presented.
| 2013 | |
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Simultaneous localization and mapping Kalman filter Navigation Atmospheric measurements Particle measurements ROBOTS MOVILES ALGORITMOS GENETICOS |
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| Inglés | |
| Universidad de la República | |
| COLIBRI | |
| https://hdl.handle.net/20.500.12008/23039 | |
| Acceso abierto | |
| Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) |
| Sumario: | Postprint |
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