Artificial Intelligence for Vision Based Navigation

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.