Report
Self-Supervised Geometry-Guided Initialization for Robust Monocular Visual Odometry
| العنوان: | Self-Supervised Geometry-Guided Initialization for Robust Monocular Visual Odometry |
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| المؤلفون: | Kanai, Takayuki, Vasiljevic, Igor, Guizilini, Vitor, Shintani, Kazuhiro |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computer Vision and Pattern Recognition, Computer Science - Robotics |
| الوصف: | 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. Comment: 8 pages. 5 figures. This work has been submitted to the IEEE for possible publication |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2406.00929 |
| رقم الانضمام: | edsarx.2406.00929 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |