LATIDIA · Robótica

SFVO: Odometría visual de flujo estéreo guiada por confianza desacoplada con PnP bidireccional

arXiv: 2609.21754v1Tipo de anuncio: Cross Resumen: La odometría visual basada en el aprendizaje profundo (VO) ha logrado un progreso significativo, sin embargo, la mayoría de los métodos existentes se centran en un enfoque monocular, que sufre de ambigüedad de escala. S

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arXiv:2609.21754v1 Announce Type: cross Abstract: Deep learning-based visual odometry (VO) has achieved significant progress, yet most existing methods focus on a monocular approach, which suffers from scale ambiguity. Stereo VO provides real metric by its nature, but remains less studied in deep learning VO due to its high computational cost and modeling complexity. Recent advances in stereo matching and optical flow estimation have made dense visual correspondence increasingly accurate and reliable, but their complementary geometric information has not been fully exploited for VO. In this paper, we present SFVO, a correspondence-driven stereo VO framework that directly builds upon pretrained stereo matching and optical flow models. SFVO exploits pretrained stereo matching