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Detection and Tracking of Dynamic Objects by Using a Multirobot System: Application to Critical Infrastructures Surveillance

机译：通过使用多机器人系统检测和跟踪动态对象：在关键基础设施监视中的应用

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摘要

The detection and tracking of mobile objects (DATMO) is progressively gaining importance for security and surveillance applications. This article proposes a set of new algorithms and procedures for detecting and tracking mobile objects by robots that work collaboratively as part of a multirobot system. These surveillance algorithms are conceived of to work with data provided by long distance range sensors and are intended for highly reliable object detection in wide outdoor environments. Contrary to most common approaches, in which detection and tracking are done by an integrated procedure, the approach proposed here relies on a modular structure, in which detection and tracking are carried out independently, and the latter might accept input data from different detection algorithms. Two movement detection algorithms have been developed for the detection of dynamic objects by using both static and/or mobile robots. The solution to the overall problem is based on the use of a Kalman filter to predict the next state of each tracked object. Additionally, new tracking algorithms capable of combining dynamic objects lists coming from either one or various sources complete the solution. The complementary performance of the separated modular structure for detection and identification is evaluated and, finally, a selection of test examples discussed.
机译：移动对象（DATMO）的检测和跟踪在安全和监视应用中正变得越来越重要。本文提出了一套新的算法​​和过程，用于由机器人作为多机器人系统的一部分协同工作来检测和跟踪移动对象。这些监视算法旨在与远距离传感器提供的数据配合使用，旨在在宽广的室外环境中实现高度可靠的物体检测。与最常见的方法（其中检测和跟踪由集成过程完成）相反，此处提出的方法依赖于模块化结构，其中检测和跟踪是独立进行的，后者可以接受来自不同检测算法的输入数据。已经开发了两种用于通过使用静态和/或移动机器人来检测动态物体的运动检测算法。整体问题的解决方案基于卡尔曼滤波器的使用来预测每个被跟踪物体的下一个状态。此外，能够组合来自一个或多个来源的动态对象列表的新跟踪算法完善了该解决方案。评估了分离的模块化结构用于检测和识别的互补性能，最后讨论了一些测试示例。

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