Place recognition with event-based cameras and a neural implementation of SeqSLAM (bibtex)
by M Milford, H Kim, M Mangan, S Leutenegger, T Stone, B Webb and A Davison
Reference:
Place recognition with event-based cameras and a neural implementation of SeqSLAM (M Milford, H Kim, M Mangan, S Leutenegger, T Stone, B Webb and A Davison), In Innovative Sensing for Robotics: Focus on Neuromorphic Sensors Workshop at IEEE International Conference on Robotics and Automation (ICRA), 2015. 
Bibtex Entry:
@inproceedings{milford2015place,
 title = {Place recognition with event-based cameras and a neural implementation of SeqSLAM},
 author = {M Milford and H Kim and M Mangan and S Leutenegger and T Stone and B Webb and A Davison},
 booktitle = {Innovative Sensing for Robotics: Focus on Neuromorphic Sensors Workshop at IEEE International Conference on Robotics and Automation (ICRA)},
 year = {2015},
 keywords = {eventcameras},
}
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Place recognition with event-based cameras and a neural implementation of SeqSLAM (bibtex)
Place recognition with event-based cameras and a neural implementation of SeqSLAM (bibtex)
by M Milford, H Kim, M Mangan, S Leutenegger, T Stone, B Webb and A Davison
Reference:
Place recognition with event-based cameras and a neural implementation of SeqSLAM (M Milford, H Kim, M Mangan, S Leutenegger, T Stone, B Webb and A Davison), In Innovative Sensing for Robotics: Focus on Neuromorphic Sensors Workshop at IEEE International Conference on Robotics and Automation (ICRA), 2015. 
Bibtex Entry:
@inproceedings{milford2015place,
 title = {Place recognition with event-based cameras and a neural implementation of SeqSLAM},
 author = {M Milford and H Kim and M Mangan and S Leutenegger and T Stone and B Webb and A Davison},
 booktitle = {Innovative Sensing for Robotics: Focus on Neuromorphic Sensors Workshop at IEEE International Conference on Robotics and Automation (ICRA)},
 year = {2015},
 keywords = {eventcameras},
}
Powered by bibtexbrowser
Place recognition with event-based cameras and a neural implementation of SeqSLAM (bibtex)
Place recognition with event-based cameras and a neural implementation of SeqSLAM (bibtex)
by M Milford, H Kim, M Mangan, S Leutenegger, T Stone, B Webb and A Davison
Reference:
Place recognition with event-based cameras and a neural implementation of SeqSLAM (M Milford, H Kim, M Mangan, S Leutenegger, T Stone, B Webb and A Davison), In Innovative Sensing for Robotics: Focus on Neuromorphic Sensors Workshop at IEEE International Conference on Robotics and Automation (ICRA), 2015. 
Bibtex Entry:
@inproceedings{milford2015place,
 title = {Place recognition with event-based cameras and a neural implementation of SeqSLAM},
 author = {M Milford and H Kim and M Mangan and S Leutenegger and T Stone and B Webb and A Davison},
 booktitle = {Innovative Sensing for Robotics: Focus on Neuromorphic Sensors Workshop at IEEE International Conference on Robotics and Automation (ICRA)},
 year = {2015},
 keywords = {eventcameras},
}
Powered by bibtexbrowser
research:multisensorslam

Multi-Sensor SLAM

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:

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: