  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.
