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

• URL: http://arxiv.org/abs/2406.00929v1
• Date: Mon, 3 Jun 2024 01:59:29 GMT
• Title: Self-Supervised Geometry-Guided Initialization for Robust Monocular Visual Odometry
• Authors: Takayuki Kanai, Igor Vasiljevic, Vitor Guizilini, Kazuhiro Shintani,
• Abstract summary: In this paper, we analyze major failure cases on outdoor benchmarks and expose shortcomings of a learning-based SLAM model (DROID-SLAM) We propose the use of self-supervised priors leveraging a frozen large-scale pre-trained monocular depth estimation to initialize the dense bundle adjustment process. Despite its simplicity, our proposed method demonstrates significant improvements on KITTI odometry, as well as the challenging DDAD benchmark.
• Score: 9.79428015716139
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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