Localization in challenging, natural environments such as forests or woodlands is an important capability for many applications from guiding a robot navigating along a forest trail to monitoring vegetation growth with handheld sensors. In this work we explore laser-based localization in both urban and natural environments, which is suitable for online applications. We propose a [...]
We are seeking two full-time Postdoctoral Research Assistants in Robot Learning to join the Department of Engineering Science (Central Oxford). The post is funded by EPSRC fixed-term to 28th February 2020 (in the first instance). You will be a key member of the Applied Artificial Intelligence Lab (A2I) at the Oxford Robotics Institute (ORI). [...]
Each year the ORI hopes to sponsor a number of summer undergraduate internships. These are open to University of Oxford undergraduates in their third and fourth years. Non University of Oxford students may still apply, however they must secure external funding and will have to arrange their own travel and accommodation. If you wish to apply [...]
ORI has been working with BAM Construct UK to 3D map their neighbouring construction site as part of our survey and reconstruction research. We aim to use the data we are collecting to build 3D models from the datasets and compare them, not only to show how the site has changed over time, [...]
Research and Trials Assistant
We used a NABU sensor attached to a street sweeper to map the streets of Oxford, in an innovative data gathering project which will help the City Council to plan and manage its services. Read the Council's Press Release here: PRESS RELEASE 11 March 2017 Innovative new city mapping could transform council services A [...]
Ten of Oxford’s AIMS (Autonomous Intelligent Machines and Systems) CDT students joined us for our annual robotics challenge. Divided into three teams, the students had to programme a Husky robot to work its way around obstacles to reach a goal. There was some careful planning.
http://www.youtube.com/watch?v=2MSWTKBMNeE In practice, we found dense reconstructions are the most complete and highest quality (with our mobile robotics platform) when fusing data from multiple Velodyne, SICK LMS- 151, and stereo cameras using our own datasets. We are releasing such a dataset to provide a realistic mobile-robotics platform with a variety of sensors—a [...]