ポーズのみの教師あり学習による顕著スパース視覚オドメトリ
Salient Sparse Visual Odometry With Pose-Only Supervision
ポーズ情報のみを教師信号として使うハイブリッド視覚オドメトリを提案し、自己教師ありホモグラフィ事前学習とランダムパッチ顕著点検出により、密なオプティカルフロー教師なしでも高い汎化性能を実現した。
著者: Siyu Chen, Kangcheng Liu, Chen Wang, Shenghai Yuan, Jianfei Yang, Lihua Xie
分類: cs.CV, cs.RO
原文アブストラクト
Visual Odometry (VO) is vital for the navigation of autonomous systems, providing accurate position and orientation estimates at reasonable costs. While traditional VO methods excel in some conditions, they struggle with challenges like variable lighting and motion blur. Deep learning-based VO, though more adaptable, can face generalization problems in new environments. Addressing these drawbacks, this paper presents a novel hybrid visual odometry (VO) framework that leverages pose-only supervision, offering a balanced solution between robustness and the need for extensive labeling. We propose two cost-effective and innovative designs: a self-supervised homographic pre-training for enhancing optical flow learning from pose-only labels and a random patch-based salient point detection strategy for more accurate optical flow patch extraction. These designs eliminate the need for dense optical flow labels for training and significantly improve the generalization capability of the system in diverse and challenging environments. Our pose-only supervised method achieves competitive performance on standard datasets and greater robustness and generalization ability in extreme and unseen scenarios, even compared to dense optical flow-supervised state-of-the-art methods.
