My research
interests are usually around three related areas: robotics, wearable computing
and computer vision. Robots and Wearables face very
similar challenges, they usually have to work in real-time under reduced
computational, energy and sensing resources, and therefore, similar techniques
for reasoning and sensing can be applied in both. On the other side, Computer
Vision studies how to understand and represent the world using visual sensors,
and for a number of reasons, cameras are very interesting sensors: they are
commonplace, cheap and small, while being able to recover a plethora of
information, e.g. 3D structure, object ID, self and object motion and serve as
input devices for human-computer (or robot) interaction.
Following are some examples of my recent work that was done with a number of
collaborators to whom I am deeply grateful.
In-situ interactive model building
This work with PishedBunnun, is about developing real-time techniques that allow the
construction and immediate use of the models created from mobile devices.
Much work has been done to enable interactive modeling away from the object
of interest, and usually using a desktop machine. Here we want to allow
mobile users to capture models of objects quickly and accurately with reduced
computational demand.
Discovering high level structure, its effects and
applications in real-time visual SLAM
This work with Andrew P.
Gee,Denis Chekhlov and Andrew Calway aims to assess the effects and
benefits of the discovery of high level structure such as planes and lines
from the simple features commonly used in visual SLAM.
So far, this allow us to reduce the state space which benefits scalability
and also to explore applications in augmented reality such as autonomous
virtual agents being able to interact more reallistically
with the created maps in real-time.
This work with Andrew P.
Gee 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.
A. P. Gee and W.W. Mayol.
Real-Time Model-Based SLAM Using Line Segments [PDF]. To appear in LNCS
proceedings of the 2nd International Symposium on Visual Computing.
November. 2006 .
Click image for video (50MB)
Robust Real-Time SLAM Using Multiresolution
Descriptors
This
work in collaboration with Denis Chekhlov, Mark Pupilli and Andrew Calway, uses SIFT-like descriptors within a
coherent top-down framework. The resulting system provides superior
performance over previous methods in terms of robustness to erratic motion,
camera shake, and the ability to recover from periods of measurement loss.
Denis Chekhlov,
Mark Pupilli, WalterioMayol-Cuevas and Andrew Calway.
Robust Real-Time Visual SLAM Using Scale Prediction and Exemplar
Based Feature Description[PDF]. [VIDEO] In proceedings IEEE Computer
Vision and Pattern Recognition CVPR, Minneapolis, Minnesota,
USA.
2007.
Denis Chekhlov,
Mark Pupilli, WalterioMayol-Cuevas and Andrew Calway.
Real-Time and Robust Monocular SLAM Using Predictive Multi-resolution
Descriptors[PDF]. To appear in LNCS proceedings of
the 2nd International Symposium on Visual Computing, November
2006.
Hand Activity Detection with a wearable active camera.
This work aims towards the automatic detection of hand activity as observed
by a wearable camera. A Probabilistic approach fuses different cues to infer
the current activity based in the objects subject to manipulation.
W.W. Mayol and
D.W. Murray. Wearable Hand Activity Recognition for Event
Summarization [PDF]. Proc. IEEE Int. Symposium on
Wearable Computers (ISWC). Osaka,
Japan,
October 2005.
Click image for video
Hand gesture and "grasping
vector" detection.
This work aimed at detecting hand gestures in real time as seen by an active
wearable camera.
W.W. Mayol, A.J.
Davison, B.J. Tordoff, N.D.Molton, and D.W. Murray. Interaction
between hand and wearable camera in 2D and 3D environments [PDF]. Proc. British Machine Vision
Conference 2004. London,
UK,
September. 2004.
Click images for videos
General Regression for Image
Tracking.
This work developed a method to track planar and near-planar image regions by
posing the problem as one of statistical general regression. The method is
fast and robust to occlusions and rapid object motions.
W. W. Mayol
and D. W. Murray.
Tracking with General Regression. Journal of Machine Vision and
Applications, 19(1). ISSN 09328092, pp. 65-72. January 2008. Published
Online June 2007.[PDF]
Note: This work was actually done at the end of 2002 and a technical
report from then is available upon request.
Click image for video
Annotating 3D scenes in real time
using hand gestures.
This work used hand gesture recognition to add virtual objects on top of a
scene also recovered in real time. The system uses a passive wearable
wide-angle camera and Andrew Davison's SLAM
algorithm.
W.W. Mayol, A.J.
Davison, B.J. Tordoff, N.D.Molton, and D.W. Murray. Interaction
between hand and wearable camera in 2D and 3D environments [PDF]. Proc. British Machine Vision
Conference 2004. London,
UK,
September. 2004.
Click image for video
Simultaneous Localisation and Mapping
with a Wearable Active Vision Camera.
This research was done
together with Andrew Davison and presented the first
real-time simultaneous localisation and mapping
system for an active vision camera. The intended example application is
remote collaboration where a remote expert observes the world through the
wearable robot and adds augmented reality annotations to be seen by the
wearer.
A.J. Davison, W.W. Mayol,
and D.W. Murray. Real-Time Localisation and
Mapping with Wearable Active Vision [PDF]. Proc IEEE International Symposium
on Mixed and Augmented Reality (ISMAR), Tokyo, Japan,
October 7 - 10, 2003.
W.W. Mayol, A.J.
Davison, B.J. Tordoff, and D.W. Murray. Applaying Active Vision and SLAM to Wearables. Proc. 11th Int. Symposium of Robotics
Research ISRR03. Siena,
Italy,
19-22 October. 2003.
Wearable camera placement. This work studies different
constraints involved in deciding where to place a camera (or another optical
device) around the human shape.
W.W. Mayol, B. Tordoff and D.W. Murray. On the Positioning of
wearable Optical Devices. Technical report OUEL224101.2001.
W.W. Mayol, B. Tordoff and D.W. Murray. Designing a Miniature
Wearable Visual Robot [PDF]. Proc. IEEE International Conference on
Robotics and Automation ICRA2002, WashingtonD.C.USA.
2002.
Objective Design of an Active Vision Robot using Pareto non-dominance analysis.
In this work, a method
to design the morphology of a miniature active vision camera is presented.
The method uses the Pareto non-dominance test to evaluate seven criteria such
as range of motion, working volume, power consumption etc. to achieve an optimal (in the multi-criteria sense) robot
shape.
W.W. Mayol, B. Tordoff and D.W. Murray. Designing a Miniature
Wearable Visual Robot [PDF]. Proc. IEEE International
Conference on Robotics and Automation ICRA2002, WashingtonD.C.USA. 2002.
Click image for video
Click image for video
Wearable Visual Robots
This research developed a new type of robot to be used as an interface for
wearable computing. The robot has three degrees of freedom: elevation, paning and cyclotorsion
(rotation around the camera's optic axis). It also has a 2D accelerometer to
detect its orientation relative to the gravity vector, and a more recent
version has a fire-wire interface for the camera sensor and a full 3D
orientation sensor. These allows to have a level of
decoupling between the sensor and the motions and posture of the wearer.
W.W. Mayol, B. Tordoff, T.E. de Campos A.J. Davison, and D.W.
Murray. Active Vision for Wearables
[PDF]. Proc IEE Eurowearable03, BirminghamUK,
September 4-5, 2003.
W.W. Mayol, B. Tordoff and D.W. Murray. Wearable Visual Robots [PDF] . IEEE International Symposium on Wearable
Computing ISWC00 .AtlantaGA, USA.
October 2000.
