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cs.CV2019

Rolling-Shutter Modelling for Direct Visual-Inertial Odometry

David Schubert, Nikolaus Demmel, Lukas von Stumberg +2

We present a direct visual-inertial odometry (VIO) method which estimates the motion of the sensor setup and sparse 3D geometry of the environment based on measurements from a roll…

cs.CV2019

Efficient Derivative Computation for Cumulative B-Splines on Lie Groups

Christiane Sommer, Vladyslav Usenko, David Schubert +2

Continuous-time trajectory representation has recently gained popularity for tasks where the fusion of high-frame-rate sensors and multiple unsynchronized devices is required. Lie…

cs.CV2019

Visual-Inertial Mapping with Non-Linear Factor Recovery

Vladyslav Usenko, Nikolaus Demmel, David Schubert +2

Cameras and inertial measurement units are complementary sensors for ego-motion estimation and environment mapping. Their combination makes visual-inertial odometry (VIO) systems m…

cs.CV2018

Direct Sparse Odometry with Rolling Shutter

David Schubert, Nikolaus Demmel, Vladyslav Usenko +2

Neglecting the effects of rolling-shutter cameras for visual odometry (VO) severely degrades accuracy and robustness. In this paper, we propose a novel direct monocular VO method t…

cs.CV2018

The TUM VI Benchmark for Evaluating Visual-Inertial Odometry

David Schubert, Thore Goll, Nikolaus Demmel +3

Visual odometry and SLAM methods have a large variety of applications in domains such as augmented reality or robotics. Complementing vision sensors with inertial measurements trem…