Computing Science and Maths Seminars, 2019/2020
Seminars will take place in Room 4B96, Cottrell Building, University of Stirling. Normally, from 15.00 to 16.00 on Friday afternoons during semester time, unless otherwise stated. For instructions on how to get to the University, please look here.
Autumn 2019
| Date | Speaker | Title/Abstract |
|---|---|---|
| Friday 13th September |
Sarah Thomson | TBA |
| Friday 20th September |
Dr Ross Kelly, Department of Applied Mathematics, Liverpool John Moores University | TBA |
| Friday 27th September |
Available slot | TBA |
| Friday 4th October |
Dr Scott Denholm, SRUC | TBA |
| Friday 11th October |
Dr Marc Roper, Computer and Information Sciences, University of Strathclyde | TBA |
| Friday 18th October |
Internal events | No seminar this week |
| Friday 25th October |
Reading week | No seminar this week |
| Friday 1st November |
Available slot | |
| Friday 8th November |
Prof Annie Cuyt, University of Antwerp, Belgium | TBA |
| Friday 15th November |
Available slot | |
| Friday 22nd November |
Winter graduations | No seminar this week |
| Friday 29th November |
Dr Andrea Berardi, The Open University | TBA |
| Friday 6th December |
Georgi Tinchev, University of Oxford | Real-time LIDAR localization in natural and urban environments Localization is a key challenge in many robotics applications. In this work we explore LIDAR-based global localization in both urban and natural environments and develop a method suitable for online application. We present a method capable of achieving state-of-the-art performance while being three times faster than previous approaches, as well as occupying 70\% less memory without a significant loss of performance. Our approach leverages efficient deep learning architectures capable of learning compact point cloud descriptors directly from 3D data. The method uses an efficient feature space representation of a set of segmented point clouds to match between the current scene and the prior map. We show that down-sampling in the inner layers of the network can significantly reduce computation time without sacrificing performance. Our experiments demonstrate a factor of three reduction of computation with marginal loss in localization frequency. We evaluate the proposed methods on nine scenarios from six datasets varying between urban, park, forest and industrial environments. The proposed learning method can allow the full pipeline to run on robots with limited computation payload such as drones, quadrupeds or UGVs as it does not require a GPU at run time. |