GPS不測地域における6自由度視覚慣性ナビゲーションのためのクォータニオンベースアンセンテッドカルマンフィルタ
Quaternion-based Unscented Kalman Filter for 6-DoF Vision-based Inertial Navigation in GPS-denied Regions
GPSが使えない環境でドローンの6自由度位置・姿勢・速度を推定するため、クォータニオンを用いた計算効率の良いアンセンテッドカルマンフィルタを提案し、実データで有効性を検証した。
著者: Khashayar Ghanizadegan, Hashim A. Hashim
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
This paper investigates the orientation, position, and linear velocity estimation problem of a rigid-body moving in three-dimensional (3D) space with six degrees-of-freedom (6 DoF). The highly nonlinear navigation kinematics are formulated to ensure global representation of the navigation problem. A computationally efficient Quaternion-based Navigation Unscented Kalman Filter (QNUKF) is proposed on $\mathbb{S}^{3}\times\mathbb{R}^{3}\times\mathbb{R}^{3}$ imitating the true nonlinear navigation kinematics and utilize onboard Visual-Inertial Navigation (VIN) units to achieve successful GPS-denied navigation. The proposed QNUKF is designed in discrete form to operate based on the data fusion of photographs garnered by a vision unit (stereo or monocular camera) and information collected by a low-cost inertial measurement unit (IMU). The photographs are processed to extract feature points in 3D space, while the 6-axis IMU supplies angular velocity and accelerometer measurements expressed with respect to the body-frame. Robustness and effectiveness of the proposed QNUKF have been confirmed through experiments on a real-world dataset collected by a drone navigating in 3D and consisting of stereo images and 6-axis IMU measurements. Also, the proposed approach is validated against standard state-of-the-art filtering techniques. IEEE Keywords: Localization, Navigation, Unmanned Aerial Vehicle, Sensor-fusion, Inertial Measurement Unit, Vision Unit.
