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Computer Vision Group
TUM Department of Informatics
Technical University of Munich

Technical University of Munich



Dense Visual SLAM

The dvo_slam packages provide an implementation of our dense visual SLAM system for RGB-D cameras. The SLAM system builds upon our Dense Visual Odometry (see below). It extends the odometry approach to include a geometric error term and perform frame-to-keyframe matching. Each new keyframe is inserted into a pose graph. Additionally we search for loop closures to older keyframes. These loop closures provide additional constraints for the pose graph. The graph is incrementally optimized using the g2o framework. The output of the SLAM system are metrically consistent poses for all frame.

For source code and basic documentation visit the Github repository.

Dense Visual Odometry

The dvo packages provide an implementation of visual odometry estimation from RGB-D images for ROS. In contrast to feature-based algorithms, the approach uses all pixels of two consecutive RGB-D images to estimate the camera motion. The implementation runs in realtime on a recent CPU.

For source code and basic documentation visit the Github repository.

For questions please contact Christian Kerl.

Related Publications

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Conference and Workshop Papers
[]Dense Continuous-Time Tracking and Mapping with Rolling Shutter RGB-D Cameras (C. Kerl, J. Stueckler and D. Cremers), In IEEE International Conference on Computer Vision (ICCV), 2015. ([video][supplementary][datasets]) [bibtex] [pdf]
[]Dense Visual SLAM for RGB-D Cameras (C. Kerl, J. Sturm and D. Cremers), In Proc. of the Int. Conf. on Intelligent Robot Systems (IROS), 2013.  [bibtex] [pdf]
[]Robust Odometry Estimation for RGB-D Cameras (C. Kerl, J. Sturm and D. Cremers), In International Conference on Robotics and Automation (ICRA), 2013.  [bibtex] [pdf]Best Vision Paper Award - Finalist
[]Real-Time Visual Odometry from Dense RGB-D Images (F. Steinbruecker, J. Sturm and D. Cremers), In Workshop on Live Dense Reconstruction with Moving Cameras at the Intl. Conf. on Computer Vision (ICCV), 2011.  [bibtex] [pdf]
Other Publications
[]Odometry from RGB-D Cameras for Autonomous Quadrocopters (C. Kerl), Master's thesis, Technical University Munich, 2012.  [bibtex] [pdf]
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Informatik IX
Chair of Computer Vision & Artificial Intelligence

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

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In April 2022 Jürgen Sturm, Christian Kerl and Daniel Cremers were featured among the top 10 most influential scholars in robotics of the last decade.


We have open PhD and postdoc positions! To apply, please use our application form.


We have six papers accepted to CVPR 2022 in New Orleans!


We have two papers accepted to ICRA 2022 - congrats to Lukas von Stumberg, Qing Cheng and Niclas Zeller!