CAJ | 학술논문

在机器人同时定位与地图创建(SLAM)问题中，多机器人SLAM成为目前机器人学中的研究热点。因此，基于精确稀疏扩展信息滤波算法(ESEIF)的多机器人 SLAM 问题，根据多机器人的运动模型和观测模型分别对多机器人位姿估计及环境特征点进行观测，并依据阈值划分观测特征点，以完成机器人的观测更新，同时边缘化机器人位姿并进行重定位。实验仿真数据表明：多机器人的位姿精度良好，观测更新阶段时间基本上恒定，与地图特征点数量无关，体现了ESEIF算法在研究多机器人SLAM问题的有效性。
재궤기인동시정위여지도창건(SLAM)문제중，다궤기인SLAM성위목전궤기인학중적연구열점。인차，기우정학희소확전신식려파산법(ESEIF)적다궤기인 SLAM 문제，근거다궤기인적운동모형화관측모형분별대다궤기인위자고계급배경특정점진행관측，병의거역치화분관측특정점，이완성궤기인적관측경신，동시변연화궤기인위자병진행중정위。실험방진수거표명：다궤기인적위자정도량호，관측경신계단시간기본상항정，여지도특정점수량무관，체현료ESEIF산법재연구다궤기인SLAM문제적유효성。
In the robot simultaneous localization and mapping (SLAM) problem, the Multi-robot SLAM has drawn much attention recently in robotics. This paper investigates the Multi-robot SLAM problem in using exactly sparse extended information filters algorithm (ESEIF):Based on both the multi-robot motion model and observation model, the multi-robot pose is estimated and the environment features are observed. The threshold is set for partitioning and updating the observed features, and marginalizing the robot pose followed by relocating the robot. The simulation shows that the robots pose can be accurately estimated, and observation can be updated in a constant time fashion irrespective of the number of features in the map.