mrg-admin

/Oxford Robotics Institute

About Oxford Robotics Institute

This author has not yet filled in any details.
So far Oxford Robotics Institute has created 66 blog entries.

Road vehicle localization with 2D push-broom lidar and 3D priors

In this paper we describe and demonstrate a method for precisely localizing a road vehicle using a single push-broom 2D laser scanner while leveraging a prior 3D survey. In contrast to conventional scan matching, our laser is oriented downwards, thus causing continual ground strike. Our method exploits this to produce a small 3D swathe of laser data which can be matched [...]

Laser-only road-vehicle localization with dual 2D push-broom LIDARS and 3D priors

In this paper we consider long-term navigation using fixed 2D LIDARs. We consider how localization algorithms based on scan-matching - commonly used in indoor environments - are prone to failure when exposed to a challenging real-world outdoor environment. The driving motivation behind this work is to produce a simple, robust system that can be utilized repeatedly over a long period, rather than [...]

Continuous Vehicle Localisation Using Sparse 3D Sensing, Kernelised Renyi Distance and Fast Gauss Transforms

Abstract—This paper is about estimating a smooth, continuous-time trajectory of a vehicle relative to a prior 3D laser map. We pose the estimation problem as that of finding a sequence of Catmull-Rom splines which optimise the Kernelised Rényi Distance (KRD) between the prior map and live measurements from a 3D laser sensor. Our approach treats [...]

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 [...]