多机器人同步定位与地图构建的地图融合算法的改进
Improvement of map fusion algorithm for multi-robot simultaneous localization and mapping

本文主要研究了多机器人SLAM的地图实时融合问题。在本文中提出一种改进的HybridSLAM算法，可以同时观测和更新多个路标，并根据FastSLAM2.0思想利用选取的最准确的路标观测值来修正机器人位姿。然后，在改进HybridSLAM算法基础上，本文进一步提出一种多机器人SLAM方法（MR-IHybridSLAM）。每个机器人在不同初始位置运行IHybridSLAM算法构建子地图，并将子地图信息实时发送到同一工作站中。根据卡尔曼滤波（KF）原理将每个机器人构建的子地图融合成全局地图。最后，通过仿真实验构建多机器人融合的特征地图并与单一机器人FastSLAM和HybridSLAM算法构建的地图进行误差对比，进一步来验证该算法的准确性、快速性和可行性。
This paper mainly studies the real-time fusion of the submaps of multi-robot SLAM (simultaneous localization and mapping,). In this paper, an improved HybridSLAM algorithm is proposed, which can observe and update multiple landmarks at the same time. The improved algorithm uses the most precise observation among multiple landmarks to correct the robot pose according to the algorithm of FastSLAM2.0. Then, based on the improved HybridSLAM algorithm, this paper further proposes a multi-robot SLAM method (MR-IHybridSLAM). Each robot runs the IHybridSLAM algorithm to build a submap at different initial positions, and sends the submap information to the same workstation in real time. According to the Kalman filter (KF) principle, submaps built by each robot individually are merged into a global map. Finally, the fused map of multi-robot is constructed by simulation experiments, and subsequently the map errors of the fused map and other maps constructed by single robot FastSLAM and HybridSLAM algorithm are compared to verify the accuracy, fastness and feasibility of the algorithm