The Oxford Mobile Robotics Group (MRG) has an international reputation in the area of robot navigation especially when it comes to operating in outdoor environments with well-engineered vehicles and complex real-time software. The group is particularly strong in SLAM algorithms, the semantic labeling of places and topological appearance based mapping. Complementing the information theoretic research within MRG, the group possesses a strong culture of in-field experimental validation. MRG currently has 16 members and is supported by a range of industrial, international and UK research council funded projects. International recognition received includes the “best overall paper” award at the International Conference on Robotics and Automation in 2006, the “best vision paper award” at the same meeting in 2008 as well as a best paper award at the International Symposium on Experimental Robotics in 2006 for work.

More information can be found at: http://www.robots.ox.ac.uk/~mobile.

 

 


Publications

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Introspective Classification for Robot Perception

H. Grimmett, R. Triebel, R. Paul, and I. Posner International Journal of Robotics Research (IJRR) In robotics, the use of a classification framework which produces scores with inappropriate confidences will ultimately lead to the robot making dangerous decisions. In order to select a framework which will make the best decisions, we should pay careful attention
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Integrating Metric and Semantic Maps for Vision-Only Automated Parking

H. Grimmett, M. Buerki, L. Paz, P. Piniés, P. Furgale, I. Posner, and P. Newman CONFERENCE We present a framework for integrating two layers of map which are often required for fully automated operation: metric and semantic. Metric maps are likely to improve with subsequent visitations to the same place, while semantic maps can comprise
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Introspective Active Learning for Scalable Semantic Mapping

Rudolph Triebel, Hugo Grimmett, Rohan Paul, Ingmar Posner Workshop. Robotics Science and Systems (RSS) This paper proposes an active learning framework for semantic mapping in mobile robotics. In particular, our work explores the benefits of an introspective classifier over that of a more traditional non-introspective approach for active data selection. We extend the notion of
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Toward Automated Driving in Cities using Close-to-Market Sensors, an Overview of the V-Charge Project

Paul Furgale et al. IEEE Intelligent Vehicles Symposium (IVS) 2013 Future requirements for drastic reduction of CO2 production and energy consumption will lead to significant changes in the way we see mobility in the years to come. However, the automotive industry has identified significant barriers to the adoption of electric vehicles, including reduced driving range
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Confidence Boosting: Improving the Introspectiveness of a Boosted Classifier for Efficient Learning

Rudolph Triebel, Hugo Grimmett, and Ingmar Posner 2013 IEEE International Conference on Robotics and Automation (ICRA) This paper concerns the recently introduced notion of introspective classification. We introduce a variant of the point-biserial correlation coefficient (PBCC) as a measure to characterise the introspective capacity of a classifier and apply it to investigate further the introspective
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Knowing When We Don’t Know: Introspective Classification for Mission-Critical Decision Making

Hugo Grimmett, Rohan Paul, Rudolph Triebel, and Ingmar Posner 2013 IEEE International Conference on Robotics and Automation (ICRA) Classification precision and recall have been widely adopted by roboticists as canonical metrics to quantify the performance of learning algorithms. This paper advocates that for robotics applications, which often involve mission-critical decision making, good performance according to
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