Deep Visual SLAM Frontends: SuperPoint, SuperGlue, and SuperMaps (

Abstract: Mixed Reality and Robotics require robust Simultaneous Localization and Mapping (SLAM) capabilities, and many researchers believe that deep learning is the solution. This talk will discuss the frontend/backend distinction in Visual SLAM systems as well as discuss my team’s work on deep learning-based frontends for visual SLAM. The talk will focus on the applications of Convolutional and Graph Neural Networks for visual localization and SLAM, as well as novel self-supervised ways of training such networks. This talk will cover our work on SuperPoint and SuperGlue, and conclude with a discussion of future directions for building robust SuperMaps using deep learning concepts.

Presentation Slides: https://tom.ai/presentations/cvpr2020...

Presenter: Tomasz Malisiewicz, Ph.D.

Bio: Tomasz Malisiewicz is a Principal Engineer at Magic Leap, Inc. His research lies at the intersection of Deep Learning and SLAM. Previously he was a co-founder of VISION.AI, LLC, an object detection startup and a Postdoctoral Fellow at MIT CSAIL, working with Antonio Torralba. He received his Ph.D. in Robotics from Carnegie Mellon University in 2011, supervised by Alyosha Efros. During his Ph.D., he was a recipient of the NSF Graduate Research Fellowship, spent two summers as an intern in Google Research, as well as one semester as a visiting student at École Normale Supérieure in Paris.

Researcher homepages:
Tomasz Malisiewicz https://tom.ai
Daniel DeTone https://danieldetone.com
Paul-Edouard Sarlin https://psarlin.com

#computervision #deeplearning #slam #localization