Accurately estimating the positions of multi-agent systems in indoor environments is challenging due to the lack of Global Navigation Satelite System (GNSS) signals. Noisy measurements of position and orientation can cause the integrated position estimate to drift without bound. Previous research has proposed using magnetic field simultaneous localization and mapping (SLAM) to compensate for position drift in a single agent. Here, we propose two novel algorithms that allow multiple agents to apply magnetic field SLAM using their own and other agents measurements. Our first algorithm is a centralized approach that uses all measurements collected by all agents in a single extended Kalman filter. This algorithm simultaneously estimates the agents position and orientation and the magnetic field norm in a central unit that can communicate with all agents at all times. In cases where a central unit is not available, and there are communication drop-outs between agents, our second algorithm is a distributed approach that can be employed. We tested both algorithms by estimating the position of magnetometers carried by three people in an optical motion capture lab with simulated odometry and simulated communication dropouts between agents. We show that both algorithms are able to compensate for drift in a case where single-agent SLAM is not. We also discuss the conditions for the estimate from our distributed algorithm to converge to the estimate from the centralized algorithm, both theoretically and experimentally. Our experiments show that, for a communication drop-out rate of 80 percent, our proposed distributed algorithm, on average, provides a more accurate position estimate than single-agent SLAM. Finally, we demonstrate the drift-compensating abilities of our centralized algorithm on a real-life pedestrian localization problem with multiple agents moving inside a building.

翻译：室内环境下缺乏全球导航卫星系统信号，使得多智能体系统精确定位面临挑战。位置和方向的噪声测量会导致积分位置估计无界漂移。已有研究提出利用磁场同时定位与建图技术补偿单智能体位置漂移。本文提出两种新型算法，使多智能体能够利用自身及其他智能体的测量数据进行磁场同时定位与建图。首个算法采用集中式方法，将所有智能体采集的全部测量数据整合至单一扩展卡尔曼滤波器中，在可与所有智能体实时通信的中心单元同时估计智能体位置、方向及磁场范数。当无法使用中心单元且智能体间存在通信中断时，第二种分布式算法可发挥作用。我们在光学运动捕捉实验室中，通过模拟里程计和智能体间通信中断，测试了三人携带磁力计的位置估计性能。实验表明，两种算法均能在单智能体SLAM失效的情况下补偿漂移。我们从理论和实验两方面讨论了分布式算法估计结果收敛于集中式算法的条件。实验显示，在80%通信中断率下，所提出的分布式算法平均定位精度优于单智能体SLAM。最后，我们通过真实场景中多智能体在建筑物内移动的行人定位问题，验证了集中式算法的漂移补偿能力。