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Machine Learning for Robotics
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Technical University of Munich

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Informatik IX

Professorship for Machine Learning for Robotics

Smart Robotics Lab

Boltzmannstrasse 3
85748 Garching info@vision.in.tum.de

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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.

Current collaborators:

  • Chris Choi (SRL Imperial College London)

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)


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2020
Journal Articles
[]OKVIS 2.0 for the FPV Drone Racing VIO Competition 2020 (S Leutenegger), In , 2020.  [bibtex]
2019
Journal Articles
[]Fully autonomous micro air vehicle flight and landing on a moving target using visual–inertial estimation and model-predictive control (D Tzoumanikas, W Li, M Grimm, K Zhang, M Kovac and S Leutenegger), In Journal of Field Robotics, volume 36, 2019.  [bibtex]
Conference and Workshop Papers
[]KO-Fusion: dense visual SLAM with tightly-coupled kinematic and odometric tracking (C Houseago, M Bloesch and S Leutenegger), In 2019 International Conference on Robotics and Automation (ICRA), 2019.  [bibtex]
2017
Conference and Workshop Papers
[]Dense rgb-d-inertial slam with map deformations (T Laidlow, M Bloesch, W Li and S Leutenegger), In 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2017.  [bibtex]
2015
Journal Articles
[]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]
2014
Conference and Workshop Papers
[]A synchronized visual-inertial sensor system with FPGA pre-processing for accurate real-time SLAM (J Nikolic, J Rehder, M Burri, P Gohl, S Leutenegger, PT Furgale and R Siegwart), In 2014 IEEE international conference on robotics and automation (ICRA), 2014.  [bibtex]
PhD Thesis
[]Unmanned solar airplanes: Design and algorithms for efficient and robust autonomous operation (S Leutenegger), PhD thesis, ETH Zurich, 2014.  [bibtex]
2013
Journal Articles
[]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]
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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)


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2019
Preprints
[]Event-based vision: A survey (G Gallego, T Delbruck, G Orchard, C Bartolozzi, B Taba, A Censi, S Leutenegger, A Davison, J Conradt, K Daniilidis and others), In arXiv preprint arXiv:1904.08405, 2019.  [bibtex]
2016
Conference and Workshop Papers
[]Real-time 3D reconstruction and 6-DoF tracking with an event camera (H Kim, S Leutenegger and AJ Davison), In European Conference on Computer Vision, 2016.  [bibtex]
[]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]
2015
Preprints
[]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 arXiv preprint arXiv:1505.04548, 2015.  [bibtex]
Conference and Workshop Papers
[]Towards visual slam with event-based cameras (M Milford, H Kim, S Leutenegger and A Davison), In The problem of mobile sensors workshop in conjunction with RSS, 2015.  [bibtex]
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Informatik IX

Professorship for Machine Learning for Robotics

Smart Robotics Lab

Boltzmannstrasse 3
85748 Garching info@vision.in.tum.de

Follow us on:
SRL  CVG  DVL