Adaptive VIO: Deep Visual-Inertial Odometry with Online Continual Learning
<|reference_start|>Adaptive VIO: Deep Visual-Inertial Odometry with Online Continual Learning: Visual-inertial odometry (VIO) has demonstrated remarkable success due to its low-cost and complementary sensors. However, existing VIO methods lack the generalization ability to adjust to different environments and sensor attributes. In this paper, we propose Adaptive VIO, a new monocular visual-inertial odometry that combines online continual learning with traditional nonlinear optimization. Adaptive VIO comprises two networks to predict visual correspondence and IMU bias. Unlike end-to-end approaches that use networks to fuse the features from two modalities (camera and IMU) and predict poses directly, we combine neural networks with visual-inertial bundle adjustment in our VIO system. The optimized estimates will be fed back to the visual and IMU bias networks, refining the networks in a self-supervised manner. Such a learning-optimization-combined framework and feedback mechanism enable the system to perform online continual learning. Experiments demonstrate that our Adaptive VIO manifests adaptive capability on EuRoC and TUM-VI datasets. The overall performance exceeds the currently known learning-based VIO methods and is comparable to the state-of-the-art optimization-based methods.<|reference_end|>
@article{pan2024adaptive,
    title={Adaptive VIO: Deep Visual-Inertial Odometry with Online Continual
  Learning},
    author={Youqi Pan, Wugen Zhou, Yingdian Cao, Hongbin Zha},
    journal={arXiv preprint arXiv:2405.16754},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.16754},
    primaryClass={cs.RO}
}