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Updated: Sep 9, 2025

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Odometría visual-inercial robusta con características de línea basadas en el aprendizaje en un entorno de cambio de

Xinkai Li1, Cong Liu1, Xu Yan1

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Sensors (Basel, Switzerland)
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DeepLine-VIO mejora la odometría visual inercial (VIO) mediante el uso de características de línea aprendidas e invariantes en la iluminación. Este marco robusto mejora la precisión de la trayectoria en entornos desafiantes y de baja textura.

Palabras clave:
aprendizaje profundocaracterísticas de la línealocalización y mapeo simultáneos (SLAM)Odometría por inercia visual (VIO)

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Área de la Ciencia:

  • La robótica
  • Visión por computadora
  • Inteligencia artificial

Sus antecedentes:

  • Los sistemas de odometría visual-inercial (VIO) luchan en entornos de baja textura.
  • Los métodos existentes que utilizan características de línea se degradan bajo iluminación variable.

Objetivo del estudio:

  • Desarrollar un marco VIO robusto, DeepLine-VIO, que supere la degradación del rendimiento en condiciones visuales difíciles.
  • Mejorar la consistencia geométrica y la invarianza de la iluminación de las características visuales para VIO.

Principales métodos:

  • Integración de características de línea aprendidas, extraídas a través de una red profunda basada en el campo de atracción, con características puntuales y datos inerciales.
  • Utilización de un marco de optimización de ventana deslizante para el acoplamiento estrecho de observaciones multimodales.
  • Implementación de una estrategia de filtrado y parametrización consciente de la geometría para una extracción fiable de segmentos de línea.

Principales resultados:

  • DeepLine-VIO demuestra un rendimiento superior a los métodos VIO basados en puntos y líneas existentes en el conjunto de datos de EuRoC.
  • Reducción significativa del error absoluto de trayectoria (ATE) hasta un 15,87% y del error relativo de posición (RPE) en la traducción hasta un 58,45% bajo perturbaciones de iluminación.
  • Rendimiento superior constante en condiciones de degradación visual y cambio de iluminación.

Conclusiones:

  • DeepLine-VIO ofrece una mayor robustez y precisión para los sistemas VIO en entornos desafiantes.
  • Las características de línea aprendidas e invariables en la iluminación son críticas para mejorar el rendimiento de VIO.
  • El marco propuesto proporciona una solución fiable para la odometría visual en situaciones de deterioro visual.