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10 results (View BibTeX file of all listed publications)

2020

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Where Does It End? - Reasoning About Hidden Surfaces by Object Intersection Constraints

Strecke, M., Stückler, J.

In Proceedings IEEE/CVF Conf. on Computer Vision and Pattern Recognition (CVPR), IEEE/CVF International Conference on Computer Vision and Pattern Recognition (CVPR) 2020, June 2020, to appear (inproceedings) Accepted

preprint project page [BibTex]

2020

Strecke, M., Stückler, J. Where Does It End? - Reasoning About Hidden Surfaces by Object Intersection Constraints In Proceedings IEEE/CVF Conf. on Computer Vision and Pattern Recognition (CVPR), IEEE/CVF International Conference on Computer Vision and Pattern Recognition (CVPR) 2020, June 2020, to appear (inproceedings) Accepted

preprint project page [BibTex]

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TUM Flyers: Vision-Based MAV Navigation for Systematic Inspection of Structures

Usenko, V., Stumberg, L. V., Stückler, J., Cremers, D.

In Bringing Innovative Robotic Technologies from Research Labs to Industrial End-users: The Experience of the European Robotics Challenges, 136, pages: 189-209, Springer International Publishing, 2020 (inbook)

[BibTex]

Usenko, V., Stumberg, L. V., Stückler, J., Cremers, D. TUM Flyers: Vision-Based MAV Navigation for Systematic Inspection of Structures In Bringing Innovative Robotic Technologies from Research Labs to Industrial End-users: The Experience of the European Robotics Challenges, 136, pages: 189-209, Springer International Publishing, 2020 (inbook)

[BibTex]

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Planning from Images with Deep Latent Gaussian Process Dynamics

Bosch, N., Achterhold, J., Leal-Taixe, L., Stückler, J.

2nd Annual Conference on Learning for Dynamics and Control (L4DC) , 2020, to appear, arXiv:2005.03770 (conference) Accepted

preprint project page poster [BibTex]

Bosch, N., Achterhold, J., Leal-Taixe, L., Stückler, J. Planning from Images with Deep Latent Gaussian Process Dynamics 2nd Annual Conference on Learning for Dynamics and Control (L4DC) , 2020, to appear, arXiv:2005.03770 (conference) Accepted

preprint project page poster [BibTex]

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Visual-Inertial Mapping with Non-Linear Factor Recovery

Usenko, V., Demmel, N., Schubert, D., Stückler, J., Cremers, D.

IEEE Robotics and Automation Letters (RA-L), 5, 2020, accepted for presentation at IEEE International Conference on Robotics and Automation (ICRA) 2020, to appear, arXiv:1904.06504 (article)

Abstract
Cameras and inertial measurement units are complementary sensors for ego-motion estimation and environment mapping. Their combination makes visual-inertial odometry (VIO) systems more accurate and robust. For globally consistent mapping, however, combining visual and inertial information is not straightforward. To estimate the motion and geometry with a set of images large baselines are required. Because of that, most systems operate on keyframes that have large time intervals between each other. Inertial data on the other hand quickly degrades with the duration of the intervals and after several seconds of integration, it typically contains only little useful information. In this paper, we propose to extract relevant information for visual-inertial mapping from visual-inertial odometry using non-linear factor recovery. We reconstruct a set of non-linear factors that make an optimal approximation of the information on the trajectory accumulated by VIO. To obtain a globally consistent map we combine these factors with loop-closing constraints using bundle adjustment. The VIO factors make the roll and pitch angles of the global map observable, and improve the robustness and the accuracy of the mapping. In experiments on a public benchmark, we demonstrate superior performance of our method over the state-of-the-art approaches.

[BibTex]

Usenko, V., Demmel, N., Schubert, D., Stückler, J., Cremers, D. Visual-Inertial Mapping with Non-Linear Factor Recovery IEEE Robotics and Automation Letters (RA-L), 5, 2020, accepted for presentation at IEEE International Conference on Robotics and Automation (ICRA) 2020, to appear, arXiv:1904.06504 (article)

[BibTex]

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DirectShape: Photometric Alignment of Shape Priors for Visual Vehicle Pose and Shape Estimation

Wang, R., Yang, N., Stückler, J., Cremers, D.

In Accepted for IEEE international Conference on Robotics and Automation (ICRA), 2020, arXiv:1904.10097 (inproceedings) Accepted

[BibTex]

Wang, R., Yang, N., Stückler, J., Cremers, D. DirectShape: Photometric Alignment of Shape Priors for Visual Vehicle Pose and Shape Estimation In Accepted for IEEE international Conference on Robotics and Automation (ICRA), 2020, arXiv:1904.10097 (inproceedings) Accepted

[BibTex]

2019

EM-Fusion: Dynamic Object-Level SLAM With Probabilistic Data Association
EM-Fusion: Dynamic Object-Level SLAM With Probabilistic Data Association

Strecke, M., Stückler, J.

In International Conference on Computer Vision, October 2019, arXiv:1904.11781 (inproceedings)

preprint Project page Poster DOI [BibTex]

2019

Strecke, M., Stückler, J. EM-Fusion: Dynamic Object-Level SLAM With Probabilistic Data Association In International Conference on Computer Vision, October 2019, arXiv:1904.11781 (inproceedings)

preprint Project page Poster DOI [BibTex]

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Learning to Disentangle Latent Physical Factors for Video Prediction

Zhu, D., Munderloh, M., Rosenhahn, B., Stückler, J.

In German Conference on Pattern Recognition (GCPR), 2019, to appear (inproceedings)

dataset & evaluation code video preprint [BibTex]

Zhu, D., Munderloh, M., Rosenhahn, B., Stückler, J. Learning to Disentangle Latent Physical Factors for Video Prediction In German Conference on Pattern Recognition (GCPR), 2019, to appear (inproceedings)

dataset & evaluation code video preprint [BibTex]

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3D Birds-Eye-View Instance Segmentation

Elich, C., Engelmann, F., Kontogianni, T., Leibe, B.

In German Conference on Pattern Recognition (GCPR), 2019, arXiv:1904.02199, to appear (inproceedings)

[BibTex]

Elich, C., Engelmann, F., Kontogianni, T., Leibe, B. 3D Birds-Eye-View Instance Segmentation In German Conference on Pattern Recognition (GCPR), 2019, arXiv:1904.02199, to appear (inproceedings)

[BibTex]

2018

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Detailed Dense Inference with Convolutional Neural Networks via Discrete Wavelet Transform

Ma, L., Stueckler, J., Wu, T., Cremers, D.

arxiv, 2018, arXiv:1808.01834 (techreport)

[BibTex]

2018

Ma, L., Stueckler, J., Wu, T., Cremers, D. Detailed Dense Inference with Convolutional Neural Networks via Discrete Wavelet Transform arxiv, 2018, arXiv:1808.01834 (techreport)

[BibTex]

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Numerical Quadrature for Probabilistic Policy Search

Vinogradska, J., Bischoff, B., Achterhold, J., Koller, T., Peters, J.

IEEE Transactions on Pattern Analysis and Machine Intelligence, pages: 1-1, 2018 (article)

DOI [BibTex]

Vinogradska, J., Bischoff, B., Achterhold, J., Koller, T., Peters, J. Numerical Quadrature for Probabilistic Policy Search IEEE Transactions on Pattern Analysis and Machine Intelligence, pages: 1-1, 2018 (article)

DOI [BibTex]