State estimation for legged robots-consistent fusion of leg kinematics and IMU (bibtex)
by M Bloesch, M Hutter, MA Hoepflinger, S Leutenegger, C Gehring, CD Remy and R Siegwart
Reference:
State estimation for legged robots-consistent fusion of leg kinematics and IMU (M Bloesch, M Hutter, MA Hoepflinger, S Leutenegger, C Gehring, CD Remy and R Siegwart), In Robotics, MIT Press, volume 17, 2013. 
Bibtex Entry:
@article{bloesch2013state,
 title = {State estimation for legged robots-consistent fusion of leg kinematics and IMU},
 author = {M Bloesch and M Hutter and MA Hoepflinger and S Leutenegger and C Gehring and CD Remy and R Siegwart},
 journal = {Robotics},
 volume = {17},
 pages = {17--24},
 year = {2013},
 publisher = {MIT Press},
 keywords = {multisensorslam},
}
Powered by bibtexbrowser
State estimation for legged robots-consistent fusion of leg kinematics and IMU (bibtex)
State estimation for legged robots-consistent fusion of leg kinematics and IMU (bibtex)
by M Bloesch, M Hutter, MA Hoepflinger, S Leutenegger, C Gehring, CD Remy and R Siegwart
Reference:
State estimation for legged robots-consistent fusion of leg kinematics and IMU (M Bloesch, M Hutter, MA Hoepflinger, S Leutenegger, C Gehring, CD Remy and R Siegwart), In Robotics, MIT Press, volume 17, 2013. 
Bibtex Entry:
@article{bloesch2013state,
 title = {State estimation for legged robots-consistent fusion of leg kinematics and IMU},
 author = {M Bloesch and M Hutter and MA Hoepflinger and S Leutenegger and C Gehring and CD Remy and R Siegwart},
 journal = {Robotics},
 volume = {17},
 pages = {17--24},
 year = {2013},
 publisher = {MIT Press},
 keywords = {multisensorslam},
}
Powered by bibtexbrowser
State estimation for legged robots-consistent fusion of leg kinematics and IMU (bibtex)
State estimation for legged robots-consistent fusion of leg kinematics and IMU (bibtex)
by M Bloesch, M Hutter, MA Hoepflinger, S Leutenegger, C Gehring, CD Remy and R Siegwart
Reference:
State estimation for legged robots-consistent fusion of leg kinematics and IMU (M Bloesch, M Hutter, MA Hoepflinger, S Leutenegger, C Gehring, CD Remy and R Siegwart), In Robotics, MIT Press, volume 17, 2013. 
Bibtex Entry:
@article{bloesch2013state,
 title = {State estimation for legged robots-consistent fusion of leg kinematics and IMU},
 author = {M Bloesch and M Hutter and MA Hoepflinger and S Leutenegger and C Gehring and CD Remy and R Siegwart},
 journal = {Robotics},
 volume = {17},
 pages = {17--24},
 year = {2013},
 publisher = {MIT Press},
 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: