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Computer Vision Group
TUM School of Computation, Information and Technology
Technical University of Munich

Technical University of Munich




Contact: Lingni Ma, Robert Maier.

Our RGB-D SLAM system builds upon our Direct RGB-D 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.

Direct RGB-D Odometry

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.

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
Computer Vision Group

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

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CVPR 2023

We have six papers accepted to CVPR 2023.


NeurIPS 2022

We have two papers accepted to NeurIPS 2022.


WACV 2023

We have two papers accepted at WACV 2023.


Fulbright PULSE podcast on Prof. Cremers went online on Apple Podcasts and Spotify.


MCML Kick-Off

On July 27th, we are organizing the Kick-Off of the Munich Center for Machine Learning in the Bavarian Academy of Sciences.