论文标题
高速无人机的视觉惯性导航方法
A Visual-inertial Navigation Method for High-Speed Unmanned Aerial Vehicles
论文作者
论文摘要
本文通过单眼相机和惯性导航系统调查了高速高空无人机（UAV）的本地化问题。它提出了一种使用视觉和惯性设备的互补性来克服无人机水平飞行产生的奇异性的导航方法。此外，它通过将线性零件与非线性零件分开，并用线性相等约束的优化问题来修改本地化问题的数学模型。为了避免在顺序不受限制的最小化技术（惩罚方法）的最佳点附近的不良条件特性（惩罚方法），它使用基于差分 - 代理动力学系统的信任区域技术构建了一种半无限的连续方法，以求解线性公平的优化优化问题。它还在无限的集成间隔中分析了半密码连续方法的全局收敛性，而不是在有限的集成间隔中对普通微分方程的数值方法进行的传统收敛分析。最后，还提出了有希望的数值结果。
This paper investigates the localization problem of high-speed high-altitude unmanned aerial vehicle (UAV) with a monocular camera and inertial navigation system. It proposes a navigation method utilizing the complementarity of vision and inertial devices to overcome the singularity which arises from the horizontal flight of UAV. Furthermore, it modifies the mathematical model of localization problem via separating linear parts from nonlinear parts and replaces a nonlinear least-squares problem with a linearly equality-constrained optimization problem. In order to avoid the ill-condition property near the optimal point of sequential unconstrained minimization techniques(penalty methods), it constructs a semi-implicit continuous method with a trust-region technique based on a differential-algebraic dynamical system to solve the linearly equality-constrained optimization problem. It also analyzes the global convergence property of the semi-implicit continuous method in an infinity integrated interval other than the traditional convergence analysis of numerical methods for ordinary differential equations in a finite integrated interval. Finally, the promising numerical results are also presented.
