摘要
同时定位与地图构建(SLAM)问题在移动机器人研究领域受到了广泛关注,其是机器人能否实现完全自主运动功能的关键。首先阐述了SLAM系统相关模型,并介绍了经典卡尔曼滤波相关知识;其次介绍基于扩展卡尔曼滤波、无迹卡尔曼滤波与粒子滤波的SLAM算法如何解决现实世界的非线性、非高斯问题,并总结了各算法优缺点;最后,展望了基于卡尔曼滤波的SLAM算法发展趋势。
The problem of simultaneous localization and mapping(SLAM)in the field of mobile robotics is getting more and more attention,which is regarded as the key technology to the robot's ability to achieve fully autonomous motion functions.first of all,this article elaborates the relevant models of SLAM system,and introduces the related knowledge of classical Kalman filter.Secondly,the SLAM algorithm based on extended Kalman filter,unscented Kalman filter and particle filter is introduced to solve the nonlinear and non-Gaussian problems in the real world.The advantages and disadvantages of each algorithm are summarized.Finally,the development trend of SLAM algorithm based on Kalman filter is forecasted.
作者
孙海波
童紫原
唐守锋
童敏明
纪玉明
SUN Hai-bo;TONG Zi-yuan;TANG Shou-feng;TONG Min-ming;JI Yu-ming(School of Information and Control Engineering, China University of Mining and Technology,Xuzhou 221000,China;School of Information and Electrical Engineering,University of New South Wales, Sydney2052,Australia;Xuzhou Hanlin Technology Co. LTD,Xuzhou 221000,China)
出处
《软件导刊》
2018年第12期1-3,7,共4页
Software Guide
基金
国家重点研发计划项目(2016YFC0801800)
