Optimized Self-Localization for SLAM in Dynamic Scenes Using Probability Hypothesis Density Filters
Optimized Self-Localization for SLAM in Dynamic Scenes Using Probability Hypothesis Density Filters
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DOI：
10.1109/tsp.2017.2775590
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发表时间：
2018-02-15
影响因子：
5.4
通讯作者：
Naylor, Patrick A.
中科院分区：
文献类型：
--
作者：
Evers, Christine;Naylor, Patrick A.
在许多应用中，在未知环境中映射物体位置的传感器安装在动态平台上。由于测量是相对于观察者的传感器，场景映射需要观察者状态的准确知识。然而，在实践中，观察员的报告会出现定位误差。同时定位和映射解决了观察者定位和场景映射的联合估计问题。最先进的方法通常使用视觉或光学传感器，因此依赖于环境中的静态信标来锚观察者估计。然而，涉及传统上不用于同时定位和地图构建（SLAM）的传感器的许多应用受到高度动态场景的影响，使得静态世界假设无效。本文提出了一种新的方法，称为GEneralized Motion（GEM）SLAM。基于概率假设密度滤波器，所提出的方法概率锚观测器状态融合观测器的运动报告从场景推断的信息。本文推导了GEM-SLAM的一般理论框架，并表明它推广了现有的基于概率假设密度（PHD）的SLAM算法。使用距离方位传感器和多个移动对象的模型特定实现的仿真突出显示，GEM-SLAM实现了三个基准算法的显着改进。
In many applications, sensors that map the positions of objects in unknown environments are installed on dynamic platforms. As measurements are relative to the observer's sensors, scene mapping requires accurate knowledge of the observer state. However, in practice, observer reports are subject to positioning errors. Simultaneous localization and mapping addresses the joint estimation problem of observer localization and scene mapping. State-of-the-art approaches typically use visual or optical sensors and therefore rely on static beacons in the environment to anchor the observer estimate. However, many applications involving sensors that are not conventionally used for Simultaneous Localization and Mapping (SLAM) are affected by highly dynamic scenes, such that the static world assumption is invalid. This paper proposes a novel approach for dynamic scenes, called GEneralized Motion (GEM) SLAM. Based on probability hypothesis density filters, the proposed approach probabilistically anchors the observer state by fusing observer information inferred from the scene with reports of the observer motion. This paper derives the general, theoretical framework for GEM-SLAM, and shows that it generalizes existing Probability Hypothesis Density (PHD)-based SLAM algorithms. Simulations for a model-specific realization using range-bearing sensors and multiple moving objects highlight that GEM-SLAM achieves significant improvements over three benchmark algorithms.