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AI Seminar

Probabilistic Robotics with Applications To Navigation

Sebastian Thrun, Prof. of Computer Science, Stanford Univ.
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NOTE CHANGE IN LOCATION: TOYOTA TECHNICAL CENTER

Bayesian techniques have often been heralded as the most significant innovation in robotics software over the past decade. Probabilistic techniques model the inherent uncertainty in our models of the world, and they also model the inherent uncertainty in sensor data. As such, they tend to be superior to many classical techniques that ignore uncertainty in our world models, and they are superior to many reactive techniques that ignore the uncertainty in sensor data. This talk will introduce the audience into a rich body of work in the area of robotic navigation, mapping, and localization, and discuss recent work on precision localization of self-driving cars.

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