多机器人SLAM后端优化算法综述

对于移动机器人研究领域来说,现阶段研究热点是如何在全球定位系统失效的情况下同时定位与地图构建(simultaneous localization and mapping,SLAM)。对于单个机器人SLAM已经有很多解决方案,然而当转移到多机器人平台时,对于存在的问题又面临很多新的挑战。本文首先分析了多机器人SLAM,着重探讨了多机器人SLAM后端优化算法。分析了多机器人SLAM研究过程中遇到的不同问题,以及现阶段这些问题的处理算法。讨论了多机器人SLAM中扩展卡尔曼滤波、扩展信息滤波、粒子滤波、基于图优化的SLAM、地图融合等后端优化算法的研究现状,分析了算法的优缺点,并提出了未来发展的方向。

Survey on multi-robot SLAM back-end optimization algorithm

One of the main issues for researchers in the field of mobile robots is the SLAM in the environment of GPS-denied. There are many solutions for single robot SLAM, however, when it is transferred to multi-robot platforms, there are a lot of new challenges to the existing problems. The most advanced multi-robot system is introduced in this paper, and the problem of multi-robot SLAM is studied. The different problems encountered in the research of multi-robot SLAM are introduced, and the solutions to these problems are discussed. In this paper, the research status of extended kalman filter(EKF)-SLAM, extended information filtering(EIF)-SLAM, particle filter(PF)-SLAM, graphSLAM and map fusion and other back-end optimization algorithms is discussed, the advantages and disadvantages of the algorithm are analyzed, and the possible direction of improvement is put forward.