tinyML Asia 2021
Lightweight visual localization with deep learning
Yihong WU, Professor, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, and at School of Artificial Intelligence, University of Chinese Academy of Sciences
Virtual reality (VR), augmented reality (AR), robotics, and autonomous driving have recently attracted much attention from the academic as well as an industrial community. Visual localization or SLAM(Simultaneous localization and mapping) plays important role in these fields. While tremendous progress in autonomous navigation has been made in the past, many challenges remain. In this talk, I will present our recent research efforts on taking up these challenges. At first, I will give an overview of visual localization with learning, then introduce a fast Localization (or SLAM relocalization) in large scale environments by leveraging local and global CNN descriptors in parallel with co-training both real and binary descriptors, and then introduce a flexible and efficient loop closure detection based on motion knowledge with CNN Hash codes. Also, a robust SLAM system with accurate and fast feature tracking is presented. Finally, future trends for visual localization are also shared.