论文标题
快速适应非线性观察者
Fast Adaptation Nonlinear Observer for SLAM
论文作者
论文摘要
同时在三维（3D）空间中映射环境并定位移动车辆的姿势（方向和位置）的过程称为同时定位和映射（SLAM）。 SLAM是机器人应用程序中的核心任务。在大满贯问题中，假定车辆的每个姿势和环境是完全未知的。本文将常规的大满贯设计作为基础，并提出了一种新的方法，以确保对SLAM的非线性观察者进行快速适应。由于真正的大满贯问题是非线性的，并且在$ \ mathbb {slam} _ {n} _ {n} \ left（3 \ right）$的Lie Group上进行了建模，因此SLAM的拟议观察者是非线性的，并在$ \ Mathbb {slam} _ {slam} _ {n} _ {n} _ \ weft（3 \ right（3 \ right）上）。所提出的观察者补偿了与速度测量相关的未知偏差。模拟的结果说明了所提出的方法的鲁棒性。
The process of simultaneously mapping the environment in three dimensional (3D) space and localizing a moving vehicle's pose (orientation and position) is termed Simultaneous Localization and Mapping (SLAM). SLAM is a core task in robotics applications. In the SLAM problem, each of the vehicle's pose and the environment are assumed to be completely unknown. This paper takes the conventional SLAM design as a basis and proposes a novel approach that ensures fast adaptation of the nonlinear observer for SLAM. Due to the fact that the true SLAM problem is nonlinear and is modeled on the Lie group of $\mathbb{SLAM}_{n}\left(3\right)$, the proposed observer for SLAM is nonlinear and modeled on $\mathbb{SLAM}_{n}\left(3\right)$. The proposed observer compensates for unknown bias attached to velocity measurements. The results of the simulation illustrate the robustness of the proposed approach.
