ROFT-VINS: 過酷な環境向けの頑健な特徴追跡に基づく視覚慣性状態推定
ROFT-VINS: Robust Feature Tracking-based Visual-Inertial State Estimation for Harsh Environment
深層学習を用いて単眼カメラ画像の視覚特徴を頑健に追跡し、テクスチャのない環境や急激な照明変化でも安定して動作するVIOシステムを提案した。
詳しい要約
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2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
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著者: Sanghyun Park, Soohee Han
分類: cs.RO
原文アブストラクト
SLAM (Simultaneous Localization and Mapping) and Odometry are important systems for estimating the position of mobile devices, such as robots and cars, utilizing one or more sensors. Particularly in camera-based SLAM or Odometry, effectively tracking visual features is important as it significantly impacts system performance. In this paper, we propose a method that leverages deep learning to robustly track visual features in monocular camera images. This method operates reliably even in textureless environments and situations with rapid lighting changes. Additionally, we evaluate the performance of our proposed method by integrating it into VINS-Fusion (Monocular-Inertial), a commonly used Visual-Inertial Odometry (VIO) system.
