Self-Supervised Geometry-Guided Initialization for Robust Monocular Visual Odometry

2024-06-03

arXiv.org

AI Summary

Monocular visual odometry suffers from insufficient robustness under large motions, dynamic scenes, and challenging illumination conditions. This paper proposes a geometry-guided self-supervised initialization method that, for the first time, integrates a frozen pre-trained monocular depth model (e.g., MiDaS) as a geometric prior to initialize depth and pose for dense bundle adjustment—without fine-tuning the SLAM backbone network. Leveraging self-supervised learning and explicit geometric constraints, the approach effectively suppresses motion blur artifacts and mitigates interference from dynamic objects. Evaluated on KITTI and the challenging DDAD benchmarks, the method achieves significant improvements in pose accuracy, particularly enabling stable tracking under rapid ego-motion and outdoor dynamic scenarios. Results demonstrate both the efficacy of incorporating frozen depth priors directly into the optimization pipeline and the strong generalization capability of the proposed framework.

Technology Category

• Computer Vision: Motion & Tracking
• Search and Optimization: Learning to Search
• Intelligent Robots: State Estimation

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

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.

Problem

• Addresses failures in monocular visual odometry under poor conditions
• Improves robustness against large motion and object dynamics
• Enhances dense bundle adjustment initialization using self-supervised priors

Innovation

• Self-supervised geometry-guided initialization for bundle adjustment
• Leveraging pre-trained monocular depth estimator priors
• Enhancing robustness without fine-tuning SLAM backbone