Selective sensor fusion for neural visual-inertial odometry

Deep learning approaches for Visual-Inertial Odometry (VIO) have proven successful, but they rarely focus on incorporating robust fusion strategies for dealing with imperfect input sensory data. We propose a novel end-to-end selective sensor fusion framework for monocular VIO, which fuses monocular...

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Bibliografische gegevens
Hoofdauteurs: Chen, C, Rosa, S, Miao, Y, Lu, CX, Wu, W, Markham, A, Trigoni, N
Formaat: Conference item
Taal:English
Gepubliceerd in: IEEE 2019

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