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You can watch this seminar here at 1PM EST (10AM PST) on May 15th. Abstract: To enable the next generation of smart robots and devices which can truly interact with their environments, Simultaneous Localisation and Mapping (SLAM) will progressively develop into a general real-time geometric and semantic Spatial AI' perception capability. I will give many examples from our work on gradually increasing visual SLAM capability over the years. However, much research must still be done to achieve true Spatial AI performance. A key issue is how estimation and machine learning components can be used and trained together as we continue to search for the best long-term scene representations to enable intelligent interaction.

Simultaneous localization and mapping (SLAM) is the computational problem of constructing or updating a map of an unknown environment while simultaneously keeping track of a robot's location within it. SLAM is being gradually developed towards Spatial AI, the common sense spatial reasoning that will enable robots and other artificial devices to operate in general ways in their environments. This will enable robots to not just localize and build geometric maps, but actually interact intelligently with scenes and objects. A key technology that is helping this progress is deep learning, which has enabled many recent breakthroughs in computer vision and other areas of AI. In the context of Spatial AI, deep learning has most obviously had a big impact on bringing semantic meaning to geometric maps of the world.