The semantic PHD filter for multi-class target tracking: From theory to practice
The semantic PHD filter for multi-class target tracking: From theory to practice
复制标题
DOI：
10.1016/j.robot.2021.103947
复制
发表时间：
2021-12
期刊：
影响因子：
--
通讯作者：
Jun Chen;Zhanteng Xie;P. Dames
中科院分区：
文献类型：
--
作者：
Jun Chen;Zhanteng Xie;P. Dames
为了让移动机器人能够在复杂、动态的环境中有效地运行，它必须能够理解它们周围的物体在哪里以及它们周围有什么物体。在本文中，我们引入了语义概率假设密度(SPHD)过滤器，它允许机器人同时跟踪多类目标，而不考虑测量的不确定性，包括误报检测、漏报检测、测量噪声和目标误分类。SPHD滤波器能够为每种类型的目标合并不同的运动模型，并且能够在目标数目未知且随时间变化的情况下工作。为了验证SPHD滤波器的有效性，我们对包含静态和动态目标的多种目标类型进行了仿真和硬件测试。结果表明，即使在检测误差达到一定程度的情况下，SPHD滤波器也可以有效地跟踪多类目标，并且性能优于并行运行的一组PHD滤波器，每个目标类一个PHD滤波器。我们还提供了一种详细的方法，从业者可以使用它来匹配运行SPHD过滤器所需的概率传感器模型。
In order for a mobile robot to be able to effectively operate in complex, dynamic environments it must be capable of understanding both where and what the objects around them are. In this paper we introduce the semantic probability hypothesis density (SPHD) filter, which allows robots to simultaneously track multiple classes of targets despite measurement uncertainty, including false positive detections, false negative detections, measurement noise, and target misclassification. The SPHD filter is capable of incorporating a different motion model for each type of target and of functioning in situations where the number of targets is unknown and time-varying. To demonstrate the efficacy of the SPHD filter, we conduct both simulated and hardware tests with multiple target types containing both static and dynamic targets. We show that the SPHD filter allows effective tracking of multiple classes of targets even with detection error to some level, and performs better than a collection of PHD filters running in parallel, one for each target class. We also provide a detailed methodology that practitioners can use to fit the probabilistic sensor models necessary to run the SPHD filter.