Localisation

//Localisation

FAB-MAP 3D: Topological Mapping with Spatial and Visual Appearance

Abstract— This paper describes a probabilistic framework for appearance based navigation and mapping using spatial and visual appearance data. Like much recent work on appearance based navigation we adopt a bag-of-words approach in which positive or negative observations of visual words in a scene are used to discriminate between already visited and new places. In [...]

FAB-MAP 3D: Topological Mapping with Spatial and Visual Appearance2016-10-22T19:49:35+01:00

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 [...]

Continuous Vehicle Localisation Using Sparse 3D Sensing, Kernelised Renyi Distance and Fast Gauss Transforms2016-10-22T19:49:35+01:00

Generation and Exploitation of Local Orthographic Imagery for Road Vehicle Localisation

    This work performs visual localisation using synthesised local orthographic imagery. We exploit state of the art stereo visual odometry (VO) on our survey vehicle to generate high precision synthetic orthographic images of the road surface as would be seen from overhead. The fidelity and detail of these images far exceeds that of aerial photographs. When [...]

Generation and Exploitation of Local Orthographic Imagery for Road Vehicle Localisation2016-10-22T19:49:35+01:00

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 [...]

Dealing with Shadows: Capturing Intrinsic Scene Appearance for Image-based Outdoor Localisation2016-10-22T19:51:54+01:00

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 [...]

Experienced Based Navigation2018-06-20T15:10:57+01:00

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 [...]

Distraction Suppression for Vision-Based Pose Estimation at City Scales2017-03-21T12:05:14+01:00

Robust, Long-Term Visual Localisation using Illumination Invariance

This work is about extending the reach and endurance of outdoor localisation using stereo vision. At the heart of the localisation is the fundamental task of discovering feature correspondences between recorded and live images. One aspect of this problem involves deciding where to look for correspondences in an image and the second is deciding what [...]

Robust, Long-Term Visual Localisation using Illumination Invariance2018-07-10T09:17:20+01:00

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. [...]

Learning Place-Dependent Feature Detectors for Localisation Across Extreme Lighting and Weather Conditions2018-07-10T09:16:32+01:00