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...
| 主要な著者: | , , , , , , |
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| フォーマット: | Conference item |
| 言語: | English |
| 出版事項: |
IEEE
2019
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