Corpus ID: 17112447

VINet: Visual-Inertial Odometry as a Sequence-to-Sequence Learning Problem

@inproceedings{Clark2017VINetVO,
  title={VINet: Visual-Inertial Odometry as a Sequence-to-Sequence Learning Problem},
  author={Ronald Clark and S. Wang and Hongkai Wen and A. Markham and A. Trigoni},
  booktitle={AAAI},
  year={2017}
}
In this paper we present an on-manifold sequence-to-sequence learning approach to motion estimation using visual and inertial sensors. It is to the best of our knowledge the first end-to-end trainable method for visual-inertial odometry which performs fusion of the data at an intermediate feature-representation level. Our method has numerous advantages over traditional approaches. Specifically, it eliminates the need for tedious manual synchronization of the camera and IMU as well as… Expand
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