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

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Informatik IX
Computer Vision Group

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

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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.

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research:lsdslam [2015/03/12 08:43]
engelj
research:lsdslam [2015/07/27 22:44] (current)
engelj
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- +~~REDIRECT>research:vslam:lsdslam~~
- +
-====== LSD-SLAM: Large-Scale Direct Monocular SLAM ====== +
-**Contact:** [[members:engelj]], [[schoepst@in.tum.de|Thomas Schöps]], [[members:cremers]] +
- +
-**LSD-SLAM** is a novel, direct monocular SLAM technique: Instead of using keypoints, it directly operates on image intensities both for tracking and mapping. The camera is tracked using **direct image alignment**, while geometry is estimated in the form of **semi-dense depth maps**, obtained by **filtering** over many pixelwise stereo comparisons. We then build a **Sim(3) pose-graph of keyframes**, which allows to build scale-drift corrected, large-scale maps including loop-closures. +
-LSD-SLAM runs in **real-time on a CPU**, and even on a modern smartphone. +
-<html><center><span style="color:red;font-size:15pt"><b>Code Available (see below)!</b></span></center></html> +
- +
-<html><center><iframe width="640" height="360" src="//www.youtube.com/embed/GnuQzP3gty4" frameborder="0" allowfullscreen></iframe></center></html> +
- +
-<html><br><br><h1 class="sectionedit1">Difference to keypoint-based methods</h1></html> +
-<html><center></html>{{:research:lsdslam:directvskp.png?500|}}<html></center><br></html> +
-As direct method, LSD-SLAM uses all information in the image, including e.g. edges -- while keypoint-based approaches can only use small patches around corners.  +
-This leads to higher accuracy and more robustness in sparsely textured environments (e.g. indoors), and a much denser 3D reconstruction. Further, as the proposed piselwise depth-filters incorporate many small-baseline stereo comparisons instead of only few large-baseline frames, there are much less outliers.  +
- +
-<html><br><br><h1 class="sectionedit1">Building a global map</h1></html> +
-[[http://vision.in.tum.de/_media/research/lsdslam/pointcloud.jpg|{{:research:lsdslam:pointcloud.jpg?650|}}]] +
-[[http://vision.in.tum.de/_media/research/lsdslam/depthmaps.jpg|{{:research:lsdslam:depthmaps.jpg?650|}}]] +
-(click on the images for full resolution) +
- +
-LSD-SLAM builds a pose-graph of keyframes, each containing an estimated semi-dense depth map. Using a novel direct image alignment forumlation, we directly track Sim(3)-constraints between keyframes (i.e., rigid body motion + scale), which are used to build a pose-graph which is then optimized. This formulation allows to detect and correct substantial scale-drift after large loop-closures, and to deal with large scale-variation within the same map. +
- +
-<html><br><br><h1 class="sectionedit1">Mobile Implementation</h1></html> +
-The approach even runs on a smartphone, where it can be used for AR. The estimated semi-dense depth maps are in-painted and completed with an estimated ground-plane, which then allows to implement basic physical interaction with the environment. +
- +
-<html><center><iframe width="640" height="360" src="//www.youtube.com/embed/X0hx2vxxTMg" frameborder="0" allowfullscreen></iframe></center></html> +
- +
- +
-<html><br><br><h1 class="sectionedit1">Software</h1></html> +
-LSD-SLAM is on github: +
-[[http://github.com/tum-vision/lsd_slam]] +
- +
-We support only ROS-based build system tested on Ubuntu 12.04 or 14.04 and ROS Indigo or Fuerte. However, ROS is only used for input (video), output (pointcloud & poses) and parameter handling; ROS-dependent code is tightly wrapped and can easily be replaced. To avoid overhead from maintaining different build-systems however, we do not offer an out-of-the-box ROS-free version. Android-specific optimizations and AR integration are not part of the open-source release. +
- +
-Detailled installation and usage instructions can be found in the README.md, including descriptions of the most important parameters. For best results, we recommend using a monochrome [[http://en.ids-imaging.com/store/ui-1221le.html|global-shutter camera]] with [[http://www.lensation.de/images/PDF/BM2420.pdf|fisheye lens]]. +
- +
-If you use our code, please cite our respective publications (see below). We are excited to see what you do with LSD-SLAM, if you want drop us a quick hint if you have nice videos / pictures / models / applications. +
- +
- +
-<html><br><br><h1 class="sectionedit1">Datasets</h1></html> +
-To get you started, we provide some example sequences including the input video and camera calibration, the complete generated pointcloud to be displayed with the ''lsd_slam_viewer'', as well as a (sparsified) pointcloud as .ply, which can be displayed e.g. using meshlab. +
- +
-Hint: Use ''rosbag play -r 25 X_pc.bag'' while the ''lsd_slam_viewer'' is running to replay the result of real-time SLAM at 25x speed, building up the full reconstruction whithin seconds. +
- +
-  * **Desk Sequence (0:55min, 640x480 @ 50fps)**  +
-    * <html><center><iframe width="640" height="360" src="//www.youtube.com/embed/UacKN2WDLCg" frameborder="0" allowfullscreen></iframe></center></html> +
-    * Video: [[http://vmcremers8.informatik.tu-muenchen.de/lsd/LSD_room.bag.zip|[.bag]]]  [[http://vmcremers8.informatik.tu-muenchen.de/lsd/LSD_room_images.zip|[.png]]] +
-    * Pointcloud: [[http://vmcremers8.informatik.tu-muenchen.de/lsd/LSD_room_pc.bag.zip|[.bag]]]  [[http://vmcremers8.informatik.tu-muenchen.de/lsd/LSD_room_pc.ply|[.ply]]] +
- +
-  * **Machine Sequence (2:20min, 640x480 @ 50fps)** +
-    * <html><center><iframe width="640" height="360" src="//www.youtube.com/embed/6KRlwqubLIU" frameborder="0" allowfullscreen></iframe></center></html> +
-    * Download Video: [[http://vmcremers8.informatik.tu-muenchen.de/lsd/LSD_machine.bag.zip|[.bag]]]  [[http://vmcremers8.informatik.tu-muenchen.de/lsd/LSD_machine_images.zip|[.png]]] +
-    * Download Pointcloud: [[http://vmcremers8.informatik.tu-muenchen.de/lsd/LSD_machine_pc.bag.zip|[.bag]]]  [[http://vmcremers8.informatik.tu-muenchen.de/lsd/LSD_machine_pc.ply|[.ply]]] +
- +
-  * **Foodcourt Sequence (12min, 640x480 @ 50fps)** +
-    * <html><center><iframe width="640" height="360" src="//www.youtube.com/embed/aBVXfqumTXc" frameborder="0" allowfullscreen></iframe></center></html> +
-    * Download Video: [[http://vmcremers8.informatik.tu-muenchen.de/lsd/LSD_foodcourt.bag.zip|[.bag]]]  [[http://vmcremers8.informatik.tu-muenchen.de/lsd/LSD_foodcourt_images.zip|[.png]]] +
-    * Download Pointcloud: [[http://vmcremers8.informatik.tu-muenchen.de/lsd/LSD_foodcourt_pc.bag.zip|[.bag]]]  [[http://vmcremers8.informatik.tu-muenchen.de/lsd/LSD_foodcourt_pc.ply|[.ply]]] +
- +
-  * **ECCV Sequence (7:00min, 640x480 @ 50fps)**  +
-    * <html><center><iframe width="640" height="360" src="//www.youtube.com/embed/isHXcv_AeFg" frameborder="0" allowfullscreen></iframe></center></html> +
-    * **Enable FabMap for large loop-closures for this sequence!** +
-    * Video: [[http://vmcremers8.informatik.tu-muenchen.de/lsd/LSD_eccv.bag.zip|[.bag]]]  [[http://vmcremers8.informatik.tu-muenchen.de/lsd/LSD_eccv_images.zip|[.png]]] +
-    * Pointcloud: [[http://vmcremers8.informatik.tu-muenchen.de/lsd/LSD_eccv_pc.bag.zip|[.bag]]]  [[http://vmcremers8.informatik.tu-muenchen.de/lsd/LSD_eccv_pc.ply|[.ply]]] +
- +
- +
- +
-<html><br><br><h1 class="sectionedit1">License</h1></html> +
-LSD-SLAM is released under the GPLv3 license. A professional version under a different licensing agreement intended for commercial use is available [[http://www.fablitec.com/products/lsd-slam/|here]]. Please contact us if you are interested. +
- +
- +
- +
-====== Related publications ====== +
-<bibtex> +
-<keywords>semidense</keywords> +
-</bibtex>+

Rechte Seite

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