Keyframe-based visual–inertial odometry using nonlinear optimization (bibtex)
by S Leutenegger, S Lynen, M Bosse, R Siegwart and P Furgale
Reference:
Keyframe-based visual–inertial odometry using nonlinear optimization (S Leutenegger, S Lynen, M Bosse, R Siegwart and P Furgale), In The International Journal of Robotics Research, SAGE Publications, volume 34, 2015. 
Bibtex Entry:
@article{leutenegger2015keyframe,
 title = {Keyframe-based visual--inertial odometry using nonlinear optimization},
 author = {S Leutenegger and S Lynen and M Bosse and R Siegwart and P Furgale},
 journal = {The International Journal of Robotics Research},
 volume = {34},
 number = {3},
 pages = {314--334},
 year = {2015},
 publisher = {SAGE Publications},
 keywords = {multisensorslam},
}
Powered by bibtexbrowser
Keyframe-based visual–inertial odometry using nonlinear optimization (bibtex)
Keyframe-based visual–inertial odometry using nonlinear optimization (bibtex)
by S Leutenegger, S Lynen, M Bosse, R Siegwart and P Furgale
Reference:
Keyframe-based visual–inertial odometry using nonlinear optimization (S Leutenegger, S Lynen, M Bosse, R Siegwart and P Furgale), In The International Journal of Robotics Research, SAGE Publications, volume 34, 2015. 
Bibtex Entry:
@article{leutenegger2015keyframe,
 title = {Keyframe-based visual--inertial odometry using nonlinear optimization},
 author = {S Leutenegger and S Lynen and M Bosse and R Siegwart and P Furgale},
 journal = {The International Journal of Robotics Research},
 volume = {34},
 number = {3},
 pages = {314--334},
 year = {2015},
 publisher = {SAGE Publications},
 keywords = {multisensorslam},
}
Powered by bibtexbrowser
Keyframe-based visual–inertial odometry using nonlinear optimization (bibtex)
Keyframe-based visual–inertial odometry using nonlinear optimization (bibtex)
by S Leutenegger, S Lynen, M Bosse, R Siegwart and P Furgale
Reference:
Keyframe-based visual–inertial odometry using nonlinear optimization (S Leutenegger, S Lynen, M Bosse, R Siegwart and P Furgale), In The International Journal of Robotics Research, SAGE Publications, volume 34, 2015. 
Bibtex Entry:
@article{leutenegger2015keyframe,
 title = {Keyframe-based visual--inertial odometry using nonlinear optimization},
 author = {S Leutenegger and S Lynen and M Bosse and R Siegwart and P Furgale},
 journal = {The International Journal of Robotics Research},
 volume = {34},
 number = {3},
 pages = {314--334},
 year = {2015},
 publisher = {SAGE Publications},
 keywords = {multisensorslam},
}
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: