視覚・深度ランドマークと慣性融合による劣悪視覚環境下のナビゲーション
Vision-Depth Landmarks and Inertial Fusion for Navigation in Degraded Visual Environments
RGB-DとIMUのデータを特徴検出・記述子レベルで融合し、拡張カルマンフィルタでロボット姿勢・バイアス・ランドマークを同時推定することで、GPS不可・低照度・無テクスチャ環境でのナビゲーションを実現した。
著者: Shehryar Khattak, Christos Papachristos, Kostas Alexis
分類: cs.RO, cs.CV
原文アブストラクト
This paper proposes a method for tight fusion of visual, depth and inertial data in order to extend robotic capabilities for navigation in GPS-denied, poorly illuminated, and texture-less environments. Visual and depth information are fused at the feature detection and descriptor extraction levels to augment one sensing modality with the other. These multimodal features are then further integrated with inertial sensor cues using an extended Kalman filter to estimate the robot pose, sensor bias terms, and landmark positions simultaneously as part of the filter state. As demonstrated through a set of hand-held and Micro Aerial Vehicle experiments, the proposed algorithm is shown to perform reliably in challenging visually-degraded environments using RGB-D information from a lightweight and low-cost sensor and data from an IMU.
