Single and Cooperative Imaging based GPS Denied Localisation Solution for Autonomous Platforms.

针对自主平台的基于 GPS 拒绝定位的单一和协作成像解决方案。

• 批准号: 2285294
• 金额: --
• 依托单位: City, University of London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2285294
• 关键词: Single; Cooperative; Imaging; based; GPS

To employ the use of Visual Odometry (VO) and the Passive Thermal Imagery (TI) in order to tackle the fundamental problem of reliable and efficient camera based multi-target tracking systems. The aim is to produce a significantly more convenient solution that is capable of tracking and classifying targets between the ranges of sensors in the network. The main interference to the solution would arise from cloud and other obscurants, other problem would include varying illumination levels; this would be particularly significant should platforms wish to remain stealthy. Visual Odometry has been used in wide range of robotic applications that estimate the motion of an object from images taken at regular intervals. Due to advances in Plenoptic technology, it is likely that unison of visual Odometry and Plenoptic technology would outperform similar algorithms such as Simultaneous Localisation and Mapping (SLAM). As VO focuses on relative position(s) rather than mapping the terrain, there exists a possibility to enhance a baseline solution by the use of depth mapping. This therefore provides the possibility to create a sensor base network which can identify multiple targets and track their propagation throughout the network providing the backbone to an effective real time multi-target tracking solution. The challenge of varying illumination levels can be addressed by the use of passive thermal imagery - due to the fact TI detects an objects emittance rather than its reflection and so can be used at night. The use of TI is problematic as it would make the solution prone to low spatial resolution, history effects, variations in temperature, and low signal-to-noise ratios. This is an exciting challenge an excellent opportunity to develop new image processing methods for these particular types of sensors.The use of Deep Convolutional Neural Networks and Deep Q Learning has the potential to tackle the problem of reliable and efficient target recognition. In recent years deep learning has advanced to the point that real time object detection and classification have become feasible - even on relatively lacklustre machines. This means the combination of Deep Convolutional Neural Networks and LSTM techniques employed on the sensor network provides a possible solution to a multi-target tracking system.

利用视觉里程计 (VO) 和被动热成像 (TI) 来解决基于相机的可靠且高效的多目标跟踪系统的基本问题。目的是产生一种更加方便的解决方案，能够在网络中的传感器范围之间跟踪和分类目标。对解决方案的主要干扰来自于云和其他遮挡物，其他问题包括不同的照明水平；如果平台希望保持隐秘性，这一点尤其重要。视觉里程计已广泛应用于机器人应用中，可以根据定期拍摄的图像来估计物体的运动。由于全光技术的进步，视觉里程计和全光技术的结合很可能会优于同步定位与建图 (SLAM) 等类似算法。由于 VO 关注相对位置而不是绘制地形，因此有可能通过使用深度映射来增强基线解决方案。因此，这提供了创建传感器基础网络的可能性，该网络可以识别多个目标并跟踪它们在整个网络中的传播，为有效的实时多目标跟踪解决方案提供骨干。 不同照明水平的挑战可以通过使用被动热成像来解决 - 由于 TI 检测物体的发射而不是其反射，因此可以在夜间使用。使用 TI 是有问题的，因为它会使解决方案容易出现空间分辨率低、历史效应、温度变化和信噪比低的问题。这是一个令人兴奋的挑战，也是为这些特定类型的传感器开发新的图像处理方法的绝佳机会。深度卷积神经网络和深度 Q 学习的使用有潜力解决可靠且高效的目标识别问题。近年来，深度学习已经发展到实时目标检测和分类变得可行的程度——即使在相对乏善可陈的机器上也是如此。这意味着传感器网络上采用的深度卷积神经网络和 LSTM 技术的结合为多目标跟踪系统提供了可能的解决方案。