Artificial Intelligence for Vision Based Navigation

传统的地理定位技术（例如，GPS）在某些环境中遭受弱信号或精度降低，例如，室内，或在某些严格的情况下被拒绝。基于视觉的信息不仅可以通过简单、低成本的设备获取，而且人工智能方法具有强大的模式识别和语义感知能力。因此，如何利用基于视觉的信息来定位和导航在上述情况下是一个具有挑战性的研究课题，但可能对许多现实世界的应用有很高的影响。该项目是使用基于视觉的信息进行定位和导航与人工智能方法，具体来说，深度学习技术。近年来，深度学习技术取得了惊人的进步，并已应用于许多具有挑战性的领域，例如，自然语言处理和计算机视觉。由于其普遍的代表性，深度学习也应该在基于视觉的导航应用中发挥关键作用。在这个博士的主要工作方向。项目包括-基于深度学习的图像匹配技术用于地理定位（即，基于绝对图像的导航）;还将应用与迁移学习相关的技术来使预配准的地图之间的差距（例如，军械勘测地图或低分辨率卫星图像）和真实世界的RGB图像。该方法涉及的技术包括地标检测、图像匹配和位置估计。许多深度学习方法可以应用于这些任务，即，用于地标定位的目标检测，用于图像匹配的转移学习，以及位置估计中的一些决策算法。用于自运动的基于深度学习的光流技术（即，基于相对图像的导航）估计，这是使用视频数据的导航的关键组成部分。这种方法也提出了一些理论和实践的挑战，如数据关联，遮挡，以及缺乏直接的度量信息时，利用单目相机。使用深度学习算法估计光流也是该项目的一个有趣的子主题。利用附加传感器信息（例如，RGB-D），以协助精确定位和导航。例如，重建3D环境和检测3D物体有助于在导航过程中避开潜在的障碍物。这种方法与同时定位和映射（SLAM）密切相关，其中构建未知环境的地图，同时保持对代理位置的跟踪。

Traditional geolocalisation techniques (e.g., GPS) suffer weak signal or precision decrease in some environments, e.g., indoor, or are denied in certain strict situations. Vision based information is easy to capture only with a simple and low-cost device, but also with strong pattern recognition and semantic sensing power armed by artificial intelligence methods. Therefore, how to utilize vision-based information to localize and navigate in the above situations is a challenging research topic but potentially has high impacts on many real-world applications.The goal of this Ph.D. project is to use vision-based information for localization and navigation with artificial intelligence methods, specifically, deep learning technique. In recent years, deep learning technology has achieved stunning progress and has been applied to many challenging fields, e.g., natural language processing and computer vision. Thanks to its generalized representative power, deep learning should also play a critical role in vision based navigation applications. The main work directions in this Ph.D. project include- Deep learning based image matching technique for geolocalisation (i.e., absolute image based navigation) using existing ordnance survey maps or low-resolution satellite imagery; Transfer learning related techniques will be also applied to bring the gap between pre-registered maps (e.g., ordnance survey maps or low-resolution satellite imagery) and real-world RGB images. Techniques involved in this approach include landmark detection, image matching and position estimation. A number of deep learning methods are able to be applied in these tasks, i.e., object detection for landmark localization, transfer learning for image matching, and some decision making algorithms in position estimation.- Deep learning based optical flow technique for egomotion (i.e., relative image based navigation) estimation, which is a critical component for navigation using video data. This approach also presents several theoretical and practical challenges, such as data association, occlusions, and lack of direct metric information when exploiting monocular cameras. The estimation of optical flow with deep learning algorithms is also an interesting subtopic of the project.- 3D environment reconstruction and semantic understanding with additional sensor information (e.g., RGB-D) to assist in precise localisation and navigation. For example, to reconstruct 3D environment and detect 3D objects is helpful to avoid potential obstacles during navigation. This approach is closely related to Simultaneous Localization And Mapping (SLAM), in which a map of an unknown environment is constructed while simultaneously keeping the track of an agent's location.