面向鲁棒单目视觉里程计的自监督几何引导初始化
Self-Supervised Geometry-Guided Initialization for Robust Monocular Visual Odometry
AI总结：
针对DROID-SLAM在大幅运动和动态场景中的优化失效问题，提出用冻结预训练单目深度估计器的自监督几何先验初始化稠密BA，无需微调主干即在KITTI和DDAD上显著提升鲁棒性。
摘要：
单目视觉里程计是各类自主系统中的一项关键技术。传统基于特征的方法会因光照不佳、纹理不足和大幅运动而失效。相比之下，近期基于学习的稠密SLAM方法利用迭代稠密光束平差来处理此类失效情形，并在多种真实环境中实现鲁棒且精确的定位，而无需依赖特定领域的监督。然而，尽管具有潜力，这些方法在涉及大幅运动和物体动态性的场景中仍然表现困难。在本研究中，我们通过分析户外基准上的主要失效案例并揭示其优化过程的多种不足，诊断了流行的基于学习的稠密SLAM模型（DROID-SLAM）中的关键弱点。随后，我们提出利用自监督先验，借助冻结的大规模预训练单目深度估计器来初始化稠密光束平差过程，从而在无需微调SLAM主干网络的情况下获得鲁棒的视觉里程计。尽管所提方法简单，但它在KITTI odometry以及具有挑战性的DDAD基准上均展现出显著提升。
英文摘要：
Monocular visual odometry is a key technology in various autonomous systems. Traditional feature-based methods suffer from failures due to poor lighting, insufficient texture, and large motions. In contrast, recent learning-based dense SLAM methods exploit iterative dense bundle adjustment to address such failure cases, and achieve robust and accurate localization in a wide variety of real environments, without depending on domain-specific supervision. However, despite its potential, the methods still struggle with scenarios involving large motion and object dynamics. In this study, we diagnose key weaknesses in a popular learning-based dense 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 estimator to initialize the dense bundle adjustment process, leading to robust visual odometry without the need to fine-tune the SLAM backbone. Despite its simplicity, the proposed method demonstrates significant improvements on KITTI odometry, as well as the challenging DDAD benchmark.
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