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  • Los robots de interior requieren mapas semánticos escalables para entornos complejos.
  • Los mapas semánticos existentes son demasiado densos y computacionalmente costosos o carecen de detalle geométrico.
  • Existe una compensación entre el detalle del mapa y la eficiencia computacional, lo que limita las aplicaciones en el mundo real.

Conclusiones:

  • El marco de mapeo probabilístico centrado en el objeto ofrece una representación unificada para múltiples comportamientos robóticos.
  • Este enfoque apoya la comprensión funcional de la escena para la navegación consciente del contexto.
  • Validado en conjuntos de datos de referencia y entornos de apartamentos del mundo real, mostrando ventajas significativas.