A PHD Filter Based Localization System for Robotic Swarms
A PHD Filter Based Localization System for Robotic Swarms
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DOI：
10.1007/978-3-030-92790-5_14
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
R. T. Perera;C. Yuan;P. Stegagno
中科院分区：
文献类型：
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作者：
R. T. Perera;C. Yuan;P. Stegagno
本文提出了一种基于概率假设密度（PHD）滤波的机器人群体相对定位系统。该系统仅使用机载激光雷达和摄像头传感器收集的本地信息来识别和跟踪附近的其他群体成员。系统的多传感器设置说明了单个传感器无法提供足够的信息来同时识别队友并估计他们的位置。然而，由于机器人有限的计算能力，它还需要实现不采用复杂的计算机视觉或识别算法的传感器融合技术。PHD滤波器的使用由其固有的多传感器设置促进。此外，它与此定位系统和群设置的总体目标非常一致，不需要将唯一标识符与每个团队成员相关联。该系统在一个由四个机器人组成的团队上进行了测试。
In this paper, we present a Probability Hypothesis Density (PHD) filter based relative localization system for robotic swarms. The system is designed to use only local information collected by onboard lidar and camera sensors to identify and track other swarm members within proximity. The multi-sensor setup of the system accounts for the inability of single sensors to provide enough information for the simultaneous identification of teammates and estimation of their position. However, it also requires the implementation of sensor fusion techniques that do not employ complex computer vision or recognition algorithms, due to robots’ limited computational capabilities. The use of the PHD filter is fostered by its inherent multi-sensor setup. Moreover, it aligns well with the overall goal of this localization system and swarm setup that does not require the association of a unique identifier to each team member. The system was tested on a team of four robots.