基于matlab的移动机器人同步定位与制图仿真

Chen Chen, Yinhang Cheng
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引用次数: 5

摘要

移动机器人同步定位与映射(SLAM)问题是机器人领域最活跃的研究领域之一。在SLAM的研究与仿真中,基于matlab的仿真器因其功能全面、使用简单而得到了广泛的应用。本文列出了基于matlab的主要开源SLAM仿真器及其特性。从数据生成与导入、运动模型与观测模型、算法实现等方面具体介绍了两种具有代表性的模型。这两个模拟器的仿真结果表明,基于matlab的模拟器在机器人SLAM研究中,无论是开发新算法,还是比较不同算法的精度、一致性或收敛性,都是非常方便和有用的。介绍了matlab仿真器中广泛使用的SLAM算法,包括扩展卡尔曼滤波(EKF)、基于Unscented卡尔曼滤波(UKF)的SLAM算法和FastSLAM算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
MATLAB-based simulators for mobile robot Simultaneous Localization and Mapping
Mobile robot Simultaneous Localization and Mapping (SLAM) problem is one of the most active research areas in robotics. In the research and simulation of SLAM, MATLAB-based simulators are widely used due to their comprehensive functionalities and simple usage. In this paper, the main open source MATLAB-based simulators for SLAM and their properties are listed. Two representative ones are concretely introduced from the aspects of data creation and import, motion model and observation model, and algorithms implementation. Simulation results of these two simulators indicate that MATLAB-based simulators are convenient and helpful in the robot SLAM research when developing new algorithms and when comparing accuracy, consistency or convergence of different algorithms. The SLAM algorithms widely used in MATLAB-based simulators, including Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF) based SLAM algorithm and FastSLAM algorithm, are also introduced.
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