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.

通过分析主要的失败案例并揭示优化过程的各种缺点，我们诊断了一种流行的学习型 SLAM 模型（DROID-SLAM）的关键弱点。然后，我们提出使用自监督先验，利用冻结的大规模预训练单眼深度估计初始化稠密捆绑调整过程，从而实现鲁棒的视觉里程计，无需对 SLAM 骨干进行微调。尽管方法简单，但在 KITTI 里程计和具有挑战性的 DDAD 基准上，我们的方法显示出显著的改进。代码和预训练模型将在发表后发布。