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Robot Localisation and Mapping

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If you have a question about this talk, please contact Zoubin Ghahramani.

Advanced Machine Learning Tutorial Lecture

This tutorial will cover the area of mobile robot localisation and mapping. Beginning with the simple cases of Bayesian localisation and feature based mapping, the tutorial will progress to consider the full Simultaneous Localisation and Mapping (SLAM) problem. This is in many ways a cornerstone of current and future autonomous mobile systems and has been a subject of intense research over the last five years. If we want to send machines into a-priori unknown settings we need robust, embedded SLAM - something that still eludes us. Both Kalman and sample based approaches will be covered. The tutorial will also give time to some major outstanding issues in the area – in particular the loop closing problem (the task of deciding if the current local workspace intesects with an already mapped region) and recent work on appearance based methods.

This talk is part of the Machine Learning @ CUED series.

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