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Detection and Tracking of Dynamic Objects. A MultiRobot approach to Critical Infrastructures Surveillance.

机译:检测和跟踪动态对象。关键基础设施监视的MultiRobot方法。

摘要

Despite that Critical Infrastructures (CIs) security and surveillance are a growing concern for many countries and companies, Multi Robot Systems (MRSs) have not been yet broadly used in this type of facilities. This dissertation presents a novel study of the challenges arisen by the implementation of this type of systems and proposes solutions to specific problems.udFirst, a comprehensive analysis of different types of CIs has been carried out, emphasizing the influence of the different characteristics of the facilities in the design of a security and surveillance MRS. One of the most important needs for the surveillance of a CI is the detection of intruders. From a technical point of view this problem can be abstracted as equivalent to the Detection and Trackingudof Mobile Objects (DATMO). This dissertation proposes algorithms to solve this specific problem in a CI environment.udUsing 3D range images of the environment as input data, two detection algorithms for ground robots have been developed. These detection algorithms provide a list of moving objects in the robot detection area. Direct image differentiation and computer vision techniques are used when the robot is static.udAlternatively, multi-layer ground reconstructions are compared to detect the dynamic objects when the robot is moving. Since CIs usually spread over large areas, it is very useful to incorporate aerial vehicles in the surveillance MRS. Therefore, a moving object detection algorithm for aerial vehicles has been also developed. Thisudalgorithm compares the real optical flow obtained from a down-face oriented camera with an artificial optical flow computed using a RANSAC based homography matrix.udTwo tracking algorithms have been developed to follow the moving objects trajectories. These algorithms can efficiently handle occlusions and crossings, as welludas exchange information among robots. The multirobot tracking can be applied to any type of communication structure: centralized, decentralized or a combinationudof both. Even more, the developed tracking algorithms are independent of the detection algorithms and could be potentially used with other detection proceduresudor even with static sensors, such as cameras.udIn addition, using the 3D point clouds available to the robots, a relative localization algorithm has been developed to improve the position estimation of a given robot with observations from other robots.udAll the developed algorithms have been extensively tested in different simulated CIs using the Webots robotics simulator. Furthermore, the algorithms have also been validated with real robots operating in real scenarios.udIn conclusion, this dissertation presents a multirobot approach to Critical Infrastructure Surveillance, mainly focusing on Detecting and Tracking Dynamic Objects.
机译:尽管关键基础设施(CI)的安全性和监视已成为许多国家和公司日益关注的问题,但多机器人系统(MRS)尚未在这种类型的设施中广泛使用。本文对此类系统的实施提出了新的挑战,并提出了针对特定问题的解决方案。 ud首先,对不同类型的配置项进行了全面分析,强调了配置项不同特性的影响。设施设计安全和监视MRS。监视CI的最重要需求之一就是检测入侵者。从技术角度来看,可以将此问题抽象为等同于“检测和跟踪移动对象(DATMO)”。本文提出了一种解决CI环境中特定问题的算法。以环境的3D范围图像作为输入数据,开发了两种地面机器人检测算法。这些检测算法提供了机器人检测区域中移动物体的列表。当机器人静止时,使用直接图像区分和计算机视觉技术。 ud替代地,当机器人移动时,比较多层地面重建以检测动态对象。由于配置项通常散布在较大的区域,因此将飞行器纳入监视MRS非常有用。因此,还开发了用于飞行器的运动物体检测算法。该算法将从面向下的摄像头获得的实际光流与使用基于RANSAC的单应性矩阵计算出的人工光流进行比较。 ud已开发了两种跟踪算法来跟踪运动对象的轨迹。这些算法可以有效地处理遮挡和交叉,以及机器人之间的信息交换。多机器人跟踪可以应用于任何类型的通信结构:集中式,分散式或两者的结合 udof。更重要的是,开发的跟踪算法与检测算法无关,并且可能与其他检测程序一起使用,甚至与静态传感器(例如相机)配合使用。 ud此外,使用机器人可用的3D点云,可以进行相对定位已经开发了一种算法,以改进其他机器人的观察结果来确定给定机器人的位置。 ud所有开发的算法已使用Webots机器人模拟器在不同的模拟CI中进行了广泛测试。此外,该算法也已经在真实场景下运行的真实机器人中得到了验证。 ud最后,本文提出了一种用于关键基础设施监控的多机器人方法,主要关注于动态对象的检测和跟踪。

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