Bonn RGB-D Dynamic Dataset


The Bonn RGB-D Dynamic Dataset

Abstract: This is a dataset for RGB-D SLAM, containing highly dynamic sequences. We provide 24 dynamic sequences, where people perform different tasks, such as manipulating boxes or playing with balloons, plus 2 static sequences. For each scene we provide the ground truth pose of the sensor, recorded with an Optitrack Prime 13 motion capture system. The sequences are in the same format as the TUM RGB-D Dataset, so that the same evaluation tools can be used. Furthermore, we provide a ground truth 3D point cloud of the static environment recorded using a Leica BLK360 terrestrial laser scanner.

Related publication

If you use this dataset for your research, please cite:
Emanuele Palazzolo, Jens Behley, Philipp Lottes, Philippe Giguère, Cyrill Stachniss, “ReFusion: 3D Reconstruction in Dynamic Environments for RGB-D Cameras Exploiting Residuals”, arXiv, 2019. PDF
BibTeX:
@article{palazzolo2019arxiv,
author = {E. Palazzolo and J. Behley and P. Lottes and P. Gigu\`ere and C. Stachniss},
title = {{ReFusion: 3D Reconstruction in Dynamic Environments for RGB-D Cameras Exploiting Residuals}},
journal = {arXiv},
year = {2019},
url = {https://arxiv.org/abs/1905.02082}
}

Ground truth model
groundtruth_preview

We provide the full ground truth point cloud of 394109339 points, as well as a subsampled section of 54676774 points, more convenient for evaluation. The point clouds are in PLY ASCII format. To convert a model from the reference frame of the RGB-D sensor to the one of the ground truth model, refer to the Evaluation section below.
Download Full Point Cloud (4.0 GB).
Download Subsampled Section (676.0 MB).

Evaluation

Since the dataset is in the same format as the TUM RGB-D Dataset, for evaluating the trajectory error it is possible to use the same Python tools.
For evaluating the reconstructed model w.r.t. the ground truth, it is first necessary to transform them to the same coordinate frame. To convert a model from the reference frame of the sensor to the one of the ground truth, one can use the following transformation:
$\mathbf{T}_\mathrm{g}=\mathbf{T}_\mathrm{ROS}^{-1}\mathbf{T}_0\mathbf{T}_\mathrm{ROS}\mathbf{T}_\mathrm{m}$,
where $\mathbf{T}_\mathrm{m}$ is the transformation between the reference frame of the RGB-D sensor and the one of the markers used by the motion capture system, $\mathbf{T}_\mathrm{ROS}$ transforms the coordinate frame of the motion capture system to the one used to write the file groundtruth.txt in the sequences, $\mathbf{T}_0$ is the first pose read from the file groundtruth.txt.
The value of $\mathbf{T}_\mathrm{m}$ obtained from our calibration is the following:
$ \mathbf{T}_\mathrm{m} =
\begin{pmatrix}
1.0157 & 0.1828 & -0.2389 & 0.0113 \\
0.0009 & -0.8431 & -0.6413 & -0.0098 \\
-0.3009 & 0.6147 & -0.8085 & 0.0111 \\
0 & 0 & 0 & 1.0000
\end{pmatrix}
$.
$\mathbf{T}_\mathrm{ROS}$ is needed due to a bug in the ROS node that interfaces the framework to the motion capture system, and its value is:
$\mathbf{T}_\mathrm{ROS}=
\begin{pmatrix}
-1 & 0 & 0 & 0 \\
0 & 0 & 1 & 0 \\
0 & 1 & 0 & 0 \\
0 & 0 & 0 & 1
\end{pmatrix}
$.
Finally, $\mathbf{T}_0$ has to be read from the file groundtruth.txt included in the sequence that has to be evaluated.
To simplify this process, we provide a Python script that will compute $\mathbf{T}_\mathrm{g}$ given $\mathbf{T}_0$. The script requires numpy, numpy-quaternion and numba. The three packages can be easily installed with pip.

RGB-D Sequences

rgbd_dataset_example1
rgbd_dataset_example2
rgbd_dataset_example3
rgbd_dataset_example4
The format of the RGB-D sequences is the same as the TUM RGB-D Dataset and it is described here. The depth images are already registered w.r.t. the corresponding RGB images. The calibration of the RGB camera is the following:
fx = 542.822841
fy = 542.576870
cx = 315.593520
cy = 237.756098
d0 = 0.039903
d1 = -0.099343
d2 = -0.000730
d3 = -0.000144
d4 = 0.000000

uni_bonn_logo
All Sequences
Size: 16.4 GB
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rgbd_bonn_balloon
Name: rgbd_bonn_balloon
Size: 243.8 MB
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rgbd_bonn_balloon2
Name: rgbd_bonn_balloon2
Size: 267.0 MB
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rgbd_bonn_balloon_tracking
Name: rgbd_bonn_balloon_tracking
Size: 325.9 MB
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rgbd_bonn_balloon_tracking2
Name: rgbd_bonn_balloon_tracking2
Size: 236.4 MB
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rgbd_bonn_crowd
Name: rgbd_bonn_crowd
Size: 515.9 MB
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rgbd_bonn_crowd2
Name: rgbd_bonn_crowd2
Size: 498.4 MB
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rgbd_bonn_crowd3
Name: rgbd_bonn_crowd3
Size: 482.0 MB
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rgbd_bonn_kidnapping_box
Name: rgbd_bonn_kidnapping_box
Size: 619.0 MB
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rgbd_bonn_kidnapping_box2
Name: rgbd_bonn_kidnapping_box2
Size: 723.9 MB
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rgbd_bonn_moving_nonobstructing_box
Name: rgbd_bonn_moving_nonobstructing_box
Size: 436.2 MB
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rgbd_bonn_moving_nonobstructing_box2
Name: rgbd_bonn_moving_nonobstructing_box2
Size: 513.1 MB
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rgbd_bonn_moving_obstructing_box
Name: rgbd_bonn_moving_obstructing_box
Size: 320.8 MB
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rgbd_bonn_moving_obstructing_box2
Name: rgbd_bonn_moving_obstructing_box2
Size: 422.1 MB
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rgbd_bonn_person_tracking
Name: rgbd_bonn_person_tracking
Size: 329.5 MB
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rgbd_bonn_person_tracking2
Name: rgbd_bonn_person_tracking2
Size: 324.3 MB
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rgbd_bonn_placing_nonobstructing_box
Name: rgbd_bonn_placing_nonobstructing_box
Size: 400.8 MB
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rgbd_bonn_placing_nonobstructing_box2
Name: rgbd_bonn_placing_nonobstructing_box2
Size: 372.8 MB
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rgbd_bonn_placing_nonobstructing_box3
Name: rgbd_bonn_placing_nonobstructing_box3
Size: 369.8 MB
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rgbd_bonn_placing_obstructing_box
Name: rgbd_bonn_placing_obstructing_box
Size: 508.9 MB
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rgbd_bonn_removing_nonobstructing_box
Name: rgbd_bonn_removing_nonobstructing_box
Size: 271.7 MB
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rgbd_bonn_removing_nonobstructing_box2
Name: rgbd_bonn_removing_nonobstructing_box2
Size: 501.3 MB
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rgbd_bonn_removing_obstructing_box
Name: rgbd_bonn_removing_obstructing_box
Size: 509.9 MB
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rgbd_bonn_synchronous
Name: rgbd_bonn_synchronous
Size: 182.8 MB
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rgbd_bonn_synchronous2
Name: rgbd_bonn_synchronous2
Size: 203.5 MB
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rgbd_bonn_static
Name: rgbd_bonn_static
Size: 5.8 GB
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rgbd_bonn_static_close_far
Name: rgbd_bonn_static_close_far
Size: 1.0 GB
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igg
Institute of Geodesy
and Geoinformation
lwf
Faculty of Agriculture
ubn
University of Bonn