Deep Learning and SLAM

Recent advances in machine learning enable extracting sophisticated priors for 3D geometry from data. However, it is not clear how to combine this with traditional approaches for Simultaneous Localisation and Mapping (SLAM). This thesis explores different ways of combining learned methods with classical model-based solutions to enhance both the robustness and the accuracy of these systems.

Research area: computer vision