UL-VIO: Ultra-lightweight Visual-Inertial Odometry with Noise Robust Test-time Adaptation
<|reference_start|>UL-VIO: Ultra-lightweight Visual-Inertial Odometry with Noise Robust Test-time Adaptation: Data-driven visual-inertial odometry (VIO) has received highlights for its performance since VIOs are a crucial compartment in autonomous robots. However, their deployment on resource-constrained devices is non-trivial since large network parameters should be accommodated in the device memory. Furthermore, these networks may risk failure post-deployment due to environmental distribution shifts at test time. In light of this, we propose UL-VIO -- an ultra-lightweight (<1M) VIO network capable of test-time adaptation (TTA) based on visual-inertial consistency. Specifically, we perform model compression to the network while preserving the low-level encoder part, including all BatchNorm parameters for resource-efficient test-time adaptation. It achieves 36X smaller network size than state-of-the-art with a minute increase in error -- 1% on the KITTI dataset. For test-time adaptation, we propose to use the inertia-referred network outputs as pseudo labels and update the BatchNorm parameter for lightweight yet effective adaptation. To the best of our knowledge, this is the first work to perform noise-robust TTA on VIO. Experimental results on the KITTI, EuRoC, and Marulan datasets demonstrate the effectiveness of our resource-efficient adaptation method under diverse TTA scenarios with dynamic domain shifts.<|reference_end|>
@article{park2024ul-vio:,
    title={UL-VIO: Ultra-lightweight Visual-Inertial Odometry with Noise Robust
  Test-time Adaptation},
    author={Jinho Park and Se Young Chun and Mingoo Seok},
    journal={arXiv preprint arXiv:2409.13106},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.13106},
    primaryClass={cs.CV}
}