Building a Typical Visual SLAM Pipeline

Tuesday, May 22, 3:30 PM - 4:00 PM
Summit Track: 
Technical Insights I
Mission City Ballroom B2-B5

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, I will walk 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, I'll also cover challenges, tips, open source libraries, performance metrics and publicly available benchmark-datasets.


YoungWoo Seo

Senior Director, Hyperloop-One

Dr. YoungWoo Seo is a field-roboticist and has been building mobile robots including self-driving cars, drones, and Hyperloop-One system. He is currently the senior director of autonomous systems development at the Hyperloop-One, where he's leading the development of perception stack and mission-critical systems. Prior to joining the Hyperloop-One, YoungWoo led the development of autonomous flight stack for Autel Robotics' next-generation drone, led a team of engineers to deliver a part of the next-generation products at the Special Project Group of Apple. He holds a Doctor of Philosophy and a Masters of Science in Robotics from Carnegie Mellon University, a Masters of Computer Science from Seoul National University, and a Bachelors of Computer Science from Konkuk University in Seoul, Korea.

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