Self-Supervised Geometry-Guided Initialization for Robust Monocular Visual Odometry
<|reference_start|>Self-Supervised Geometry-Guided Initialization for Robust Monocular Visual Odometry: Monocular visual odometry is a key technology in a wide variety of autonomous systems. Relative to traditional feature-based methods, that suffer from failures due to poor lighting, insufficient texture, large motions, etc., recent learning-based SLAM methods exploit iterative dense bundle adjustment to address such failure cases and achieve robust accurate localization in a wide variety of real environments, without depending on domain-specific training data. However, despite its potential, learning-based SLAM still struggles with scenarios involving large motion and object dynamics. In this paper, we diagnose key weaknesses in a popular learning-based SLAM model (DROID-SLAM) by analyzing major failure cases on outdoor benchmarks and exposing various shortcomings of its optimization process. We then propose the use of self-supervised priors leveraging a frozen large-scale pre-trained monocular depth estimation to initialize the dense bundle adjustment process, leading to robust visual odometry without the need to fine-tune the SLAM backbone. Despite its simplicity, our proposed method demonstrates significant improvements on KITTI odometry, as well as the challenging DDAD benchmark. Code and pre-trained models will be released upon publication.<|reference_end|>
@article{kanai2024self-supervised,
    title={Self-Supervised Geometry-Guided Initialization for Robust Monocular
  Visual Odometry},
    author={Takayuki Kanai, Igor Vasiljevic, Vitor Guizilini, Kazuhiro Shintani},
    journal={arXiv preprint arXiv:2406.00929},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.00929},
    primaryClass={cs.CV cs.RO}
}