Mr. Martin Rebert Profile

Martin Rebert

at Institut Franco-Allemand de Recherches de Saint-Louis

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Publications (6)

Proceedings Article | 23 April 2020 Presentation + Paper

Prediction of MUAV flight behavior from active and passive imaging in complex environment

Martin Laurenzis, Martin Rebert, Stéphane Schertzer, Emmanuel Bacher, Frank Christnacher

Proceedings Volume 11410, 1141003 (2020) https://doi.org/10.1117/12.2558561

KEYWORDS: Motion models, Image analysis, Sensors, Aerodynamics, Cameras, Optical sensing, Detection and tracking algorithms, Sensing systems, Visual process modeling, Micro unmanned aerial vehicles, Computational imaging

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Proceedings Article | 10 October 2019 Presentation + Paper

Active and passive computational imaging for tracking and prediction of three-dimensional MUAV flight paths

Martin Laurenzis, Martin Rebert, Emmanuel Bacher, Stéphane Schertzer

Proceedings Volume 11160, 1116009 (2019) https://doi.org/10.1117/12.2532727

KEYWORDS: 3D modeling, Cameras, Detection and tracking algorithms, 3D image processing, Image analysis, Corner detection, Clouds, Short wave infrared radiation, Computational imaging, Imaging systems

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Proceedings Article | 16 July 2019 Paper

A review of the dataset available for visual odometry

Martin Rebert, David Monnin, Stéphane Bazeille, Christophe Cudel

Proceedings Volume 11172, 111720W (2019) https://doi.org/10.1117/12.2521750

KEYWORDS: Cameras, Visualization, Global Positioning System, Sensors, Robotics, Laser scanners, Navigation systems, Robots, Micro unmanned aerial vehicles, Imaging systems

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SPIE Journal Paper | 20 May 2019 Open Access

Prediction of microunmanned aerial vehicle flight behavior from two-dimensional intensity images

Martin Rebert, Stéphane Schertzer, Martin Laurenzis

OE, Vol. 58, Issue 05, 053101, (May 2019) https://doi.org/10.1117/12.10.1117/1.OE.58.5.053101

KEYWORDS: 3D modeling, Filtering (signal processing), Motion models, Magnesium, 3D image processing, Aerodynamics, Detection and tracking algorithms, Corner detection, Curium, Optical engineering

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Proceedings Article | 2 May 2019 Presentation + Paper

Tracking and prediction of small unmanned aerial vehicles' flight behavior and three-dimensional flight path from laser gated viewing images

Martin Laurenzis, Martin Rebert, Stéphane Schertzer, Frank Christnacher

Proceedings Volume 11005, 110050D (2019) https://doi.org/10.1117/12.2519273

KEYWORDS: Sensors, LIDAR, Radar, Detection and tracking algorithms, Unmanned aerial vehicles, 3D image processing, Motion models, Imaging systems

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SPIE Journal Paper | 9 April 2019

Parallax beam: a vision-based motion estimation method robust to nearly planar scenes

Martin Rebert, David Monnin, Stéphane Bazeille, Christophe Cudel

JEI, Vol. 28, Issue 02, 023030, (April 2019) https://doi.org/10.1117/12.10.1117/1.JEI.28.2.023030

KEYWORDS: Cameras, Motion estimation, Visual process modeling, Motion models, Error analysis, Machine vision, Computer vision technology, Statistical analysis, Beam analyzers, Sensors

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In computer vision, the epipolar geometry embeds the geometrical relationship between two views of a scene. This geometry is degenerated for planar scenes as they do not provide enough constraints to estimate it without ambiguity. Nearly planar scenes can provide the necessary constraints to resolve the ambiguity. But classic estimators such as the 5-point or 8-point algorithm combined with a random sampling strategy are likely to fail in this case because a large part of the scene is planar and it requires lots of trials to get a nondegenerated sample. However, the planar part can be associated with a homographic model and several links exist between the epipolar geometry and homographies. The epipolar geometry can indeed be recovered from at least two homographies or one homgraphy and two noncoplanar points. The latter fits a wider variety of scenes, as it is unsure to be able to find a second homography in the noncoplanar points. This method is called plane-and-parallax. The equivalence between the parallax and the epipolar lines allows to recover the epipole as their common intersection and the epipolar geometry. Robust implementations of the method are rarely given, and we encounter several limitations in our implementation. Noisy image features and outliers make the lines not to be concurrent in a common point. Also off-plane features are unequally influenced by the noise level. We noticed that the bigger the parallax is, the lesser the noise influence is. We, therefore, propose a model for the parallax that takes into account the noise on the features location to cope with the previous limitations. We call our method the “parallax beam.” The method is validated on the KITTI vision benchmark and on synthetic scenes with strong planar degeneracy. The results show that the parallax beam improves the estimation of the camera motion in the scene with planar degeneracy and remains usable when there is not any particular planar structure in the scene.

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