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So far Oxford Robotics Institute has created 61 blog entries.

Dealing with Shadows: Capturing Intrinsic Scene Appearance for Image-based Outdoor Localisation

  In outdoor environments shadows are common. These typically strong visual features cause considerable change in the appearance of a place, and therefore confound vision- based localisation approaches. In this work  we describe how to convert a colour image of the scene to a greyscale invariant image where pixel values are a function of underlying [...]

Driven Learning for Driving: How Introspection Improves Semantic Mapping

This paper explores the suitability of commonly employed classification methods to action-selection tasks in robotics, and argues that a classifier’s introspective capacity is a vital but as yet largely under-appreciated attribute. As illustration we propose an active learning framework for semantic mapping in mobile robotics and demonstrate it in the context of autonomous driving. In [...]

Semantic Mapping

Autonomous vehicles operating in places like parking lots can leverage of a higher-level understanding of the objects around it. For instance, the knowledge hat there is an upcoming zebra crossing should be taken into account in the vehicle’s current motion plan and speed. Also labelling of parking spots, could be crucial in other  tasks as efficient assignment of [...]

Introspection

In the context of decision making 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 to the ways in which it generates scores. Precision [...]

Experienced Based Navigation

This work addresses the difficult problem of navigation in changing, dynamic environments. Assuming the world is static in appearance results in brittle mapping and localisation systems. Change comes from many sources (dynamic objects, time of day, weather, seasons) and over different time scales (minutes, hours, days, months). In this work we look to account for these variations [...]

Distraction Suppression for Vision-Based Pose Estimation at City Scales

This work addresses the challenging problem of vision-based pose estimation in busy and distracting urban environments. By leveraging laser-generated 3D scene priors, we demonstrate how distracting objects of arbitrary types can be identified and masked in order to improve egomotion estimation. Results from data collected in central London during the Olympics show how our system [...]

Learning Place-Dependent Feature Detectors for Localisation Across Extreme Lighting and Weather Conditions

This work is about metric localisation across extreme lighting and weather conditions. The typical approach in robot vision is to use a point-feature-based system for localisation tasks. However, these system typically fail when appearance changes are too drastic. This research takes a contrary view and asks what is possible if instead we learn a bespoke detector for every place. [...]