no code implementations • ICCV 2021 • Nikolaus Demmel, David Schubert, Christiane Sommer, Daniel Cremers, Vladyslav Usenko
The square root formulation pervades three major aspects of our optimization-based sliding-window estimator: for bundle adjustment we eliminate landmark variables with nullspace projection; to store the marginalization prior we employ a matrix square root of the Hessian; and when marginalizing old poses we avoid forming normal equations and update the square root prior directly with a specialized QR decomposition.
1 code implementation • CVPR 2021 • Nikolaus Demmel, Christiane Sommer, Daniel Cremers, Vladyslav Usenko
We propose a new formulation for the bundle adjustment problem which relies on nullspace marginalization of landmark variables by QR decomposition.
6 code implementations • CVPR 2020 • Christiane Sommer, Vladyslav Usenko, David Schubert, Nikolaus Demmel, Daniel Cremers
Continuous-time trajectory representation has recently gained popularity for tasks where the fusion of high-frame-rate sensors and multiple unsynchronized devices is required.
no code implementations • 4 Nov 2019 • David Schubert, Nikolaus Demmel, Lukas von Stumberg, Vladyslav Usenko, Daniel Cremers
The visual part of the system performs a photometric bundle adjustment on a sparse set of points.
7 code implementations • 13 Apr 2019 • Vladyslav Usenko, Nikolaus Demmel, David Schubert, Jörg Stückler, Daniel Cremers
We reconstruct a set of non-linear factors that make an optimal approximation of the information on the trajectory accumulated by VIO.
no code implementations • 8 Aug 2018 • Hidenobu Matsuki, Lukas von Stumberg, Vladyslav Usenko, Jörg Stückler, Daniel Cremers
We propose a novel real-time direct monocular visual odometry for omnidirectional cameras.
no code implementations • ECCV 2018 • David Schubert, Nikolaus Demmel, Vladyslav Usenko, Jörg Stückler, Daniel Cremers
Neglecting the effects of rolling-shutter cameras for visual odometry (VO) severely degrades accuracy and robustness.
9 code implementations • 24 Jul 2018 • Vladyslav Usenko, Nikolaus Demmel, Daniel Cremers
We evaluate the model using a calibration dataset with several different lenses and compare the models using the metrics that are relevant for Visual Odometry, i. e., reprojection error, as well as computation time for projection and unprojection functions and their Jacobians.
4 code implementations • 17 Apr 2018 • David Schubert, Thore Goll, Nikolaus Demmel, Vladyslav Usenko, Jörg Stückler, Daniel Cremers
For trajectory evaluation, we also provide accurate pose ground truth from a motion capture system at high frequency (120 Hz) at the start and end of the sequences which we accurately aligned with the camera and IMU measurements.
1 code implementation • 16 Apr 2018 • Lukas von Stumberg, Vladyslav Usenko, Daniel Cremers
We present VI-DSO, a novel approach for visual-inertial odometry, which jointly estimates camera poses and sparse scene geometry by minimizing photometric and IMU measurement errors in a combined energy functional.
2 code implementations • 4 Mar 2017 • Vladyslav Usenko, Lukas von Stumberg, Andrej Pangercic, Daniel Cremers
In this paper, we present a real-time approach to local trajectory replanning for microaerial vehicles (MAVs).
Robotics
no code implementations • 26 Sep 2016 • Lukas von Stumberg, Vladyslav Usenko, Jakob Engel, Jörg Stückler, Daniel Cremers
Micro aerial vehicles (MAVs) are strongly limited in their payload and power capacity.
no code implementations • 9 Jul 2016 • Jakob Engel, Vladyslav Usenko, Daniel Cremers
We present a dataset for evaluating the tracking accuracy of monocular visual odometry and SLAM methods.