by P Bardow, AJ Davison and S Leutenegger

Reference:

Simultaneous optical flow and intensity estimation from an event camera (P Bardow, AJ Davison and S Leutenegger), In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016.

Bibtex Entry:

@inproceedings{bardow2016simultaneous,
 title = {Simultaneous optical flow and intensity estimation from an event camera},
 author = {P Bardow and AJ Davison and S Leutenegger},
 booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
 pages = {884--892},
 year = {2016},
 keywords = {eventcameras},
}

by P Bardow, AJ Davison and S Leutenegger

Reference:

Bibtex Entry:

@inproceedings{bardow2016simultaneous,
 title = {Simultaneous optical flow and intensity estimation from an event camera},
 author = {P Bardow and AJ Davison and S Leutenegger},
 booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
 pages = {884--892},
 year = {2016},
 keywords = {eventcameras},
}

by P Bardow, AJ Davison and S Leutenegger

Reference:

Bibtex Entry:

@inproceedings{bardow2016simultaneous,
 title = {Simultaneous optical flow and intensity estimation from an event camera},
 author = {P Bardow and AJ Davison and S Leutenegger},
 booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
 pages = {884--892},
 year = {2016},
 keywords = {eventcameras},
}

Multi-Sensor SLAM

Chris Choi (SRL Imperial College London)

Keyframe-Based Visual-Inertial Odometry and SLAM Using Nonlinear Optimisation

Here, we fuse inertial measurements with visual measurements: due to the complementary characteristics of these sensing modalities, they have become a popular choice for accurate SLAM in mobile robotics. While historically the problem has been addressed with filtering, advancements in visual estimation suggest that non-linear optimisation offers superior accuracy, while still tractable in complexity thanks to the sparsity of the underlying problem. Taking inspiration from these findings, we formulate a probabilistic cost function that combines reprojection error of landmarks and inertial terms. We ensure real-time operation by limiting the optimisation to a bounded window of keyframes by applying various marginalisation strategies. Keyframes may be spaced in time by arbitrary intervals, while old measurements are still kept as linearised error terms.

Former collaborators:

Simon Lynen (Previously ETH Zurich, now Google)
Dr Mike Bosse (Previously ETH Zurich, Zoox)
Dr Vincent Rabaud (Previously Willow Garage, now OpenCV Foundation)
Dr Kurt Konolige (Previously Willow Garage, Google)
Andreas Forster (Previously ETH Zurich, now Facebook)
Dr Margarita Chli (ETH Zurich)
Prof. Roland Siegwart (ETH Zurich)
Dr Paul Furgale (Previously ETH Zurich, now Facebook)

Related Publications

Optical Flow and SLAM with Event Cameras (Imperial College)

Event cameras are novel camera systems that sense intensity change independently per pixel and report these events of change — brighter or darker by a specific amount — with a very accurate timestamp. As such, they are inspired from biology (retina) and offer the potential to overcome difficulties with motion blur or dynamic range that standard frame-based cameras face.

We have been looking at two different challenges: first, we tried to simply reconstruct both video and optical flow from the events: the approach should be able to deal with any scene content. Second, we tackled reconstruction of semi-dense depth and intensity keyframes along with general camera motion, where the scene is assumed to be static — effectively SLAM with an event camera.

Former collaborators:

Patrick Bardow (Previously Dyson Robotics Lab at Imperial College London, now Google)
Prof. Andrew Davison (Imperial College London)
Hanme Kim (previously Robot Vision Group at Imperial College London, now Toyota Research Institute)