论文标题
保证同时定位和映射的性能非线性观察者
Guaranteed Performance Nonlinear Observer for Simultaneous Localization and Mapping
论文作者
论文摘要
在\ mathbb {slam} _ {n} \ left（3 \右）上开发了用于同时定位和映射（SLAM）的几何非线性观察者算法。提出的新颖解决方案估计了车辆的姿势（即态度和位置）相对于地标，同时将参考特征定位在全球框架中。所提出的歧管估计量的特征是预定义的瞬态和稳态性能。动态降低边界指导系统的误差函数，以渐近地将其从其在较大给定集中的起始位置降低到原始位置。拟议的观察者具有直接使用可用速度和特征测量值的能力。此外，它还补偿了与速度测量相关的未知恒定偏差。提出了拟议的观察者的单位QAUTERNION。数值结果揭示了所提出的观察者的有效性。 Keywords: Nonlinear filter algorithm, Nonlinear observer for Simultaneous Localization and Mapping, Nonlinear estimator, nonlinear SLAM observer on manifold, nonlinear SLAM filter on matrix Lie Group, observer design, asymptotic stability, systematic convergence, Prescribed performance function, pose estimation, attitude filter, position filter, feature filter, landmark filter, gradient based SLAM observer, gradient based观察者的猛击，自适应估计，猛击观察者，观察者大满贯框架，均等观察者，惯性视觉单位，视觉，大满贯过滤器，SE（3），SO（3）。
A geometric nonlinear observer algorithm for Simultaneous Localization and Mapping (SLAM) developed on the Lie group of \mathbb{SLAM}_{n}\left(3\right) is proposed. The presented novel solution estimates the vehicle's pose (i.e. attitude and position) with respect to landmarks simultaneously positioning the reference features in the global frame. The proposed estimator on manifold is characterized by predefined measures of transient and steady-state performance. Dynamically reducing boundaries guide the error function of the system to reduce asymptotically to the origin from its starting position within a large given set. The proposed observer has the ability to use the available velocity and feature measurements directly. Also, it compensates for unknown constant bias attached to velocity measurements. Unit-qauternion of the proposed observer is presented. Numerical results reveal effectiveness of the proposed observer. Keywords: Nonlinear filter algorithm, Nonlinear observer for Simultaneous Localization and Mapping, Nonlinear estimator, nonlinear SLAM observer on manifold, nonlinear SLAM filter on matrix Lie Group, observer design, asymptotic stability, systematic convergence, Prescribed performance function, pose estimation, attitude filter, position filter, feature filter, landmark filter, gradient based SLAM observer, gradient based observer for SLAM, adaptive estimate, SLAM observer, observer SLAM framework, equivariant observer, inertial vision unit, visual, SLAM filter, SE(3), SO(3).
