YoungWoo Seo, Senior Director at Virgin Hyperloop One, presents the “Building a Typical Visual SLAM Pipeline” tutorial at the May 2018 Embedded Vision Summit.
Maps are important for both human and robot navigation. SLAM (simultaneous localization and mapping) is one of the core techniques for map-based navigation. As SLAM algorithms have matured and hardware has improved, SLAM is spreading into many new applications, from self-driving cars to floor cleaning robots. In this talk, Seo walks through a typical pipeline for SLAM, specifically visual SLAM.
A typical visual SLAM pipeline, based on visual feature tracking, begins with extracting visual features and matching the extracted features to previously surveyed features. It then continues with estimation of the current camera poses based on the feature matching results, executing a (local) bundle adjustment to jointly optimize camera poses and map points, and lastly performing a loop-closure routine to complete maps. While explaining each of these steps, Seo also covers challenges, tips, open source libraries, performance metrics and publicly available benchmark datasets.