基于高斯过程的分布式多智能体磁场范数SLAM
Distributed multi-agent magnetic field norm SLAM with Gaussian processes
AI总结：
本文针对室内多智能体定位漂移问题，提出集中式和分布式两种基于高斯过程的磁场范数SLAM算法，实验证明二者在通信中断下均优于单智能体SLAM。
摘要：
由于缺乏全球导航卫星系统（GNSS）信号，在室内环境中准确估计多智能体系统的位置具有挑战性。位置和姿态的噪声测量会导致积分位置估计无界漂移。先前的研究提出使用磁场同时定位与建图（SLAM）来补偿单个智能体的位置漂移。本文提出两种新算法，允许多个智能体利用自身和其他智能体的测量数据应用磁场SLAM。我们的第一种算法是集中式方法，在单个扩展卡尔曼滤波器中使用所有智能体采集的全部测量数据。该算法在一个能够随时与所有智能体通信的中央单元中同时估计智能体的位置、姿态以及磁场范数。在中央单元不可用且智能体之间存在通信中断的情况下，可以采用我们的第二种算法，即分布式方法。我们通过在一个光学运动捕捉实验室中估计由三个人携带的磁力计的位置来测试这两种算法，实验中使用了模拟里程计和智能体之间的模拟通信中断。我们证明，在单智能体SLAM无法补偿漂移的情况下，两种算法均能补偿漂移。我们还从理论和实验两方面讨论了分布式算法估计收敛到集中式算法估计的条件。我们的实验表明，在通信中断率为80%的情况下，我们提出的分布式算法平均能提供比单智能体SLAM更准确的位置估计。最后，我们在一个多智能体在建筑物内移动的真实行人定位问题上展示了集中式算法的漂移补偿能力。
英文摘要：
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.
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