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

使用视觉里程计(VO)和被动热成像(TI)来解决基于相机的可靠和高效的多目标跟踪系统的根本问题。其目的是产生一种明显更方便的解决方案，能够在网络中的传感器范围内跟踪和分类目标。解决方案的主要干扰将来自云和其他遮光物，其他问题将包括不同的照明级别；如果平台希望保持隐身，这将特别重要。视觉里程计已被广泛应用于机器人应用中，这些应用从以规则间隔拍摄的图像中估计对象的运动。由于全光技术的进步，视觉里程计和全光技术的统一很可能会超过类似的算法，如同步定位和映射(SLAM)。由于VO关注的是相对位置(S)，而不是地形映射，因此存在通过使用深度映射来增强基线解决方案的可能性。因此，这提供了创建基于传感器的网络的可能性，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，从而为有效的实时多目标跟踪解决方案提供主干。由于TI探测的是物体的发射度而不是其反射，因此可以在夜间使用，因此可以通过使用被动热成像来解决照明级别变化的挑战。使用TI是有问题的，因为它会使解决方案容易受到低空间分辨率、历史影响、温度变化和低信噪比的影响。这是一个令人兴奋的挑战，是为这些特殊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学习的使用具有解决可靠和高效的目标识别问题的潜力。近年来，深度学习已经发展到了实时目标检测和分类的程度--即使在相对平淡无奇的机器上也是如此。这意味着深度卷积神经网络和LSTM技术在传感器网络中的结合为多目标跟踪系统提供了一种可能的解决方案。

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.