Search Results for author: Vladyslav Usenko

Found 13 papers, 7 papers with code

Square Root Marginalization for Sliding-Window Bundle Adjustment

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.

Square Root Bundle Adjustment for Large-Scale Reconstruction

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.

Efficient Derivative Computation for Cumulative B-Splines on Lie Groups

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.

Rolling-Shutter Modelling for Direct Visual-Inertial Odometry

no code implementations4 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.

3D geometry

Visual-Inertial Mapping with Non-Linear Factor Recovery

7 code implementations13 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.

Motion Estimation

Direct Sparse Odometry with Rolling Shutter

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.

Visual Odometry

The Double Sphere Camera Model

9 code implementations24 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.

3D Reconstruction Autonomous Driving +3

The TUM VI Benchmark for Evaluating Visual-Inertial Odometry

4 code implementations17 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.

Visual Odometry

Direct Sparse Visual-Inertial Odometry using Dynamic Marginalization

1 code implementation16 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.

Real-Time Trajectory Replanning for MAVs using Uniform B-splines and a 3D Circular Buffer

2 code implementations4 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

A Photometrically Calibrated Benchmark For Monocular Visual Odometry

no code implementations9 Jul 2016 Jakob Engel, Vladyslav Usenko, Daniel Cremers

We present a dataset for evaluating the tracking accuracy of monocular visual odometry and SLAM methods.

Monocular Visual Odometry

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