Projects
Robust Real-Time SLAM Using Scale Prediction and Exemplar Based Feature Description
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Our latest visual SLAM algorithm gives very robust performance, even
in the presence of severe occlusion of features or camera shake. The
key component is a novel utilisation of multi-resolution descriptors
in a coherent top-down framework. Scale prediction is used to speed up
feature recognition and exemplar based descriptors are used to improve
wide angled matching. The resulting system provides superior
performance over previous methods in terms of robustness to erratic
motion, camera shake, and the ability to recover from measurement
loss.
Video Example 1 Video Example 2 |
Real-Time SLAM Using Line-Segments and a Concurrently Built Model
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This work develops a monocular real-time SLAM system that uses line
segments extracted on the fly and that builds a wire-frame model of the
scene to help tracking. The use of line segments provides viewpoint
invariance and robustness to partial occlusion, whilst the model-based
tracking is fast and efficient, reducing problems associated with feature
matching and extraction.
Video Example |
Real-Time Camera Tracking Using Known 3D Models and a Particle Filter
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We present an algorithm which can track the 3D pose of a hand held camera in
real-time using predefined models of objects in the scene. The technique utilises
and extends recently developed techniques for 3D tracking with a particle filter.
The novelty is in the use of edge information for 3D tracking which has not been
achieved before within a real-time Bayesian sampling framework.
Video Example |
Real-time Visual SLAM with Resilience to Erratic Motion
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SLAM using a single camera becomes difficult when erratic motions
violate predictive motion models. This problem needs to be addressed
if visual SLAM algorithms are to be transferred from robots or mobile
vehicles onto hand-held or wearable devices.
Video Example |
Real-time Camera Tracking Using Particle Filtering
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We have developed a real-time camera tracking system based on particle
filtering. It gives robust tracking of a hand-held camera, even in the
presence of severe camera shake and occlusion. Our system is the first
to demonstrate real-time camera tracking of this form using a
sequential Monte Carlo method.
Video Example |