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TUM School of Computation, Information and Technology
Technical University of Munich

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Computer Vision Group

Boltzmannstrasse 3
85748 Garching info@vision.in.tum.de

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04.03.2024

We have twelve papers accepted to CVPR 2024. Check our publication page for more details.

18.07.2023

We have four papers accepted to ICCV 2023. Check out our publication page for more details.

02.03.2023

CVPR 2023

We have six papers accepted to CVPR 2023. Check out our publication page for more details.

15.10.2022

NeurIPS 2022

We have two papers accepted to NeurIPS 2022. Check out our publication page for more details.

15.10.2022

WACV 2023

We have two papers accepted at WACV 2023. Check out our publication page for more details.

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LDSO: Direct Sparse Odometry with Loop Closure

Contact: Brief Bio, Publications

NOTE: Code is available now.

Abstract

In this paper we present an extension of Direct Sparse Odometry (DSO) to a monocular visual SLAM system with loop closure detection and pose-graph optimization (LDSO). As a direct technique, DSO can utilize any image pixel with sufficient intensity gradient, which makes it robust even in featureless areas. LDSO retains this robustness, while at the same time ensuring repeatability of some of these points by favoring corner features in the tracking frontend. This repeatability allows to reliably detect loop closure candidates with a conventional feature-based bag-of-words (BoW) approach. Loop closure candidates are verified geometrically and Sim(3) relative pose constraints are estimated by jointly minimizing 2D and 3D geometric error terms. These constraints are fused with a co-visibility graph of relative poses extracted from DSO's sliding window optimization. Our evaluation on publicly available datasets demonstrates that the modified point selection strategy retains the tracking accuracy and robustness, and the integrated pose-graph optimization significantly reduces the accumulated rotation-, translation- and scale-drift, resulting in an overall performance comparable to state-of-the-art feature-based systems, even without global bundle adjustment.

Code

You can find the code for LDSO under a GPL license at https://github.com/tum-vision/LDSO. It is based on DSO.

Citation

If you find our work useful in your research, please consider citing:

@InProceedings{gao2018ldso,
  author = "X. Gao and R. Wang and N. Demmel and D. Cremers",
  title = "LDSO: Direct Sparse Odometry with Loop Closure",
  booktitle = "iros",
  year = "2018",
  month = "October"
}

Publication


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Conference and Workshop Papers
2021
[]Tight Integration of Feature-based Relocalization in Monocular Direct Visual Odometry (M Gladkova, R Wang, N Zeller and D Cremers), In Proc. of the IEEE International Conference on Robotics and Automation (ICRA), 2021. ([project page]) [bibtex] [arXiv:2102.01191]
2018
[]LDSO: Direct Sparse Odometry with Loop Closure (X. Gao, R. Wang, N. Demmel and D. Cremers), In International Conference on Intelligent Robots and Systems (IROS), 2018. ([arxiv][video][code][project]) [bibtex]
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Informatik IX
Computer Vision Group

Boltzmannstrasse 3
85748 Garching info@vision.in.tum.de

Follow us on:

News

04.03.2024

We have twelve papers accepted to CVPR 2024. Check our publication page for more details.

18.07.2023

We have four papers accepted to ICCV 2023. Check out our publication page for more details.

02.03.2023

CVPR 2023

We have six papers accepted to CVPR 2023. Check out our publication page for more details.

15.10.2022

NeurIPS 2022

We have two papers accepted to NeurIPS 2022. Check out our publication page for more details.

15.10.2022

WACV 2023

We have two papers accepted at WACV 2023. Check out our publication page for more details.

More