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I am planning to develop a monocular visual odometry system. Is there any indoor dataset available to the public, along with the ground truth, on which I can test my approach?

Note: I am already aware of the KITTI Vision Benchmark Suite which provides stereo data for outdoor vehicles.

If anyone has access to the datasets used in the following paper [SVO14], it would be even more great: http://rpg.ifi.uzh.ch/docs/ICRA14_Forster.pdf

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  • $\begingroup$ I think you can easily use only a single camera data from the KITTI dataset. For the ground truth you can use their quite precise GPS data. $\endgroup$ – Kozuch Mar 25 '15 at 10:26
  • $\begingroup$ I mentioned in my question that I needed an indoor dataset. $\endgroup$ – Avi Singh Mar 26 '15 at 18:59
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This is another more recent one, based on the paper A Photometrically Calibrated Benchmark For Monocular Visual Odometry by Engel et al:

http://vision.in.tum.de/data/datasets/mono-dataset

This gives both indoor and outdoor scenes, with calibration (or the corresponding sequences so you can calibrate yourself). I haven't tested it fully myself but it seems a rather complete dataset.

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http://vision.in.tum.de/data/datasets/rgbd-dataset

This is a set of recordings for the Kinect and Asus Xtion pro, which are all indoors (in offices and a hangar). It comes with precise ground truth from a motion capturing system.

The data is available as ROS bag files, but also as a tarball with png images with a text file for the trajectory.

There are also some scripts to benchmark your results against the ground truth.

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I found a monocular dataset captured from a Micro Aerial Vehicle (MAV), and it has been been made available here: http://projects.asl.ethz.ch/sfly/org/doku.php?id=mav%20datasets

In case the url stops worksing in the future, I am adding the name of the publication that came along with this dataset: "A Benchmarking Tool for MAV Visual Pose Estimation" Int. Conf. Control, Automation, Robotics and Vision Singapore, 7-10th December 2010

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I am not sure if there are monocular odometry datasets there, (I think it is mostly for standard 2D laser based SLAM), but you should check out radish.

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  • $\begingroup$ Thanks a lot! This might be useful for some future problem that I might tackle. $\endgroup$ – Avi Singh Jan 5 '15 at 14:19

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