We present a dataset for evaluating the tracking accuracy of monocular Visual Odometry (VO) and SLAM methods. It contains 50 real-world sequences comprising over 100 minutes of video, recorded across different environments – ranging from narrow indoor corridors to wide outdoor scenes.

All sequences contain mostly exploring camera motion, starting and ending at the same position: this allows to evaluate tracking accuracy via the accumulated drift from start to end, without requiring ground-truth for the full sequence.

In contrast to existing datasets, all sequences are photometrically calibrated: We provide the exposure times for each frame as reported by the sensor, the camera response function and the lens attenuation factors (vignetting). Further, we propose a simple approach to non-parametric vignette and camera response calibration, which require minimal set-up and are easy to reproduce.

• Full dataset with all 50 sequences zip (43GB)
• Code for reading and undistorting the dataset sequences; performing photometric calibration with proposed approach https://github.com/tum-vision/mono_dataset_code
• More notes on the intrinsic calibration format: https://github.com/JakobEngel/dso#31-dataset-format
• Supplementary material with ORB-SLAM and DSO results zip (2.7GB) and Matlab scripts for running evaluation zip (30MB) (14.10.2016.: We have updated the supplementary material with the fixed real-time results for ORB-SLAM, corresponding to the revised version of the papers.)

• Individual sequences:

• All calibration sequences zip (13GB)
• Individual sequences:

Unless stated otherwise, all data in the Monocular Visual Odometry Dataset is licensed under a Creative Commons 4.0 Attribution License (CC BY 4.0) and the accompanying source code is licensed under a BSD-2-Clause License.

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Journal Articles
2022
[]		DM-VIO: Delayed Marginalization Visual-Inertial Odometry (L. von Stumberg and D. Cremers), In IEEE Robotics and Automation Letters (RA-L) & International Conference on Robotics and Automation (ICRA), volume 7, 2022. ([arXiv][video][project page][supplementary][code]) [bibtex] [doi]
2018
[]		Omnidirectional DSO: Direct Sparse Odometry with Fisheye Cameras (H. Matsuki, L. von Stumberg, V. Usenko, J. Stueckler and D. Cremers), In IEEE Robotics and Automation Letters & Int. Conference on Intelligent Robots and Systems (IROS), IEEE, 2018. ([arxiv]) [bibtex] [pdf]
[]		Online Photometric Calibration of Auto Exposure Video for Realtime Visual Odometry and SLAM (P. Bergmann, R. Wang and D. Cremers), In IEEE Robotics and Automation Letters (RA-L), volume 3, 2018. (This paper was also selected by ICRA'18 for presentation at the conference.[arxiv][video][code][project]) [bibtex] [pdf]ICRA'18 Best Vision Paper Award - Finalist
[]		Direct Sparse Odometry (J. Engel, V. Koltun and D. Cremers), In IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018.  [bibtex] [pdf]
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]
2020
[]		D3VO: Deep Depth, Deep Pose and Deep Uncertainty for Monocular Visual Odometry (N. Yang, L. von Stumberg, R. Wang and D. Cremers), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020.  [bibtex] [arXiv:2003.01060] [pdf]Oral Presentation
2019
[]		Rolling-Shutter Modelling for Visual-Inertial Odometry (D. Schubert, N. Demmel, L. von Stumberg, V. Usenko and D. Cremers), In International Conference on Intelligent Robots and Systems (IROS), 2019. ([arxiv]) [bibtex] [pdf]
2018
[]		Direct Sparse Odometry With Rolling Shutter (D. Schubert, N. Demmel, V. Usenko, J. Stueckler and D. Cremers), In European Conference on Computer Vision (ECCV), 2018. ([supplementary][arxiv]) [bibtex] [pdf]Oral Presentation
[]		Deep Virtual Stereo Odometry: Leveraging Deep Depth Prediction for Monocular Direct Sparse Odometry (N. Yang, R. Wang, J. Stueckler and D. Cremers), In European Conference on Computer Vision (ECCV), 2018. ([arxiv],[supplementary],[project]) [bibtex]Oral Presentation
[]		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]
[]		Direct Sparse Visual-Inertial Odometry using Dynamic Marginalization (L. von Stumberg, V. Usenko and D. Cremers), In International Conference on Robotics and Automation (ICRA), 2018. ([supplementary][video][arxiv]) [bibtex] [pdf]
2017
[]		Stereo DSO: Large-Scale Direct Sparse Visual Odometry with Stereo Cameras (R. Wang, M. Schwörer and D. Cremers), In International Conference on Computer Vision (ICCV), 2017. ([supplementary][video][arxiv][project]) [bibtex] [pdf]
2016
[]		Direct Sparse Odometry (J. Engel, V. Koltun and D. Cremers), In arXiv:1607.02565, 2016.  [bibtex] [pdf]
[]		A Photometrically Calibrated Benchmark For Monocular Visual Odometry (J. Engel, V. Usenko and D. Cremers), In arXiv:1607.02555, 2016.  [bibtex] [pdf]