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Combining learning and geometry for robot perception and navigation
Abstract
In this talk, I will discuss some of the recent work in my group on learning-aided geometric methods for solving a range of exciting problems in robotics, such as state estimation, localization, and point cloud registration. The emergence of deep learning has provided the opportunity to leverage data for creating a form of memory for online deployment as well as estimating hard-to-measure quantities in real-time. On the other hand, when assumptions are satisfied, geometric methods can provide performance guarantees and interpretable outcomes in real-world applications. I will highlight how tight integrations of these two seemingly apart approaches are possible and will enable new opportunities for advancing robotics and autonomous systems.
feed/2023/11/08/combining_learning_and_geometry_for_robot_perception_and_navigation.txt ยท Last modified: 2023/11/12 21:23 by Horea Caramizaru
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