This software allows the accurate 3D reconstruction of configurations of markers from camera images. It uses AprilTags by Olson, that can simply be printed out and attached to walls or objects. To perform the reconstruction, you need a calibrated camera with a fixed focal length (no auto focus). The camera needs to be calibrated using the typical OpenCV camera calibration model. Given good marker planarity, accurate camera calibration, sharp high-resolution images, very high precision (<1-2mm) can be achieved.
Our software consists of two tools:
- visual_marker_detection: Performs marker detection on images in a selected folder and writes all detections to a JSON-file.
- visual_marker_mapping: Reads the marker detection file and performs the 3D reconstruction. The results of the reconstruction are again written to a JSON-file that contains the poses of the reconstructed cameras and markers.
In Ubuntu, the first three dependencies can be installed using the command
apt-get install libceres-dev libsuitesparse-dev libopencv-dev libeigen3-dev libboost-dev libboost-filesystem-dev libboost-program-options-dev libboost-system-dev python-pip
In Arch Linux, use:
-
pacman -S eigen opencv boost python-pip -
Ceres is available as an AUR package called ceres-solver.
The AprilTags dependency is automatically pulled in using flep.
You need at least
- CMake 3.0
- GCC 4.7 (?)
- Flep (see https://github.com/cfneuhaus/flep)
If you are using Ubuntu, this means that you need at least Ubuntu 14.04.
- Install flep:
git clone https://github.com/cfneuhaus/flep
pip install --user ./flep
- Install visual_marker_mapping and deps:
mkdir flep_ws && cd flep_ws && flep init
cd src
git clone https://github.com/cfneuhaus/visual_marker_mapping.git
flep get_deps
flep cmake -DCMAKE_BUILD_TYPE=Release
flep make -j5
It should be possible to build our software on Windows, given that we do not use any platform specific features, but so far we have not attempted to build it there. Please let us know if you run into problems doing this.
Our software works on project paths. A project path initially has to have the following layout:
my_project/.
my_project/camera_intrinsics.json
my_project/images/
my_project/images/your_image_1.jpg
my_project/images/anotherimage.png
...
- The images folder is supposed to contain all images that you want to use for calibration. Currently, all png and jpg files within the folder are being used. Note that they all have to have the same size, and they all have to correspond to the camera intrinsics specified in the camera_intrinsics.json file.
- The camera_intrinsics.json file is something you have to create before mapping (it is not required for detection only). See the File Formats section on how to create this one.
- Results of our tools are automatically written to the root of the project path. For example the marker detection writes a file called "marker_detections.json" to the root. The reconstruction result file is called "reconstruction.json".
Our software contains two command-line tools visual_marker_detection and visual_marker_mapping, both located in the build/bin folder, and a python script that can optionally be used to visualize the results in 3D.
visual_marker_detection:
--help: Shows a help text.--project-path: Path to the aforementioned project directory.--marker_width,--marker-height: The marker width/height in meters. This is a marker size that is written to the marker_detections.json file with every marker. It is not used in any other way by the detection right now, but we feel that this information is an essential part of the detection result, which is why we include it. The marker can be configured to be slightly non-square, which can be used to compensate for bad and slightly distorted print-outs. If you have markers with different sizes, you will have to edit the marker_detections.json file by hand. If you do not care about the metrical size if your reconstruction, you can simply set both the width/height to any value, say 0.1, or simply stick to the default value.--do-corner-refinement: We noticed that the AprilTags library we used, has issues with very high-resolution images, like DSLR images of slightly non-planar markers. To counteract this issue, we have added OpenCVs corner refinement to our software. We only recommend using flag in the described case though. For lower resolution images or planar markers, existing corner localization method is fine.--marker_type: Allows to configure the type of markers that are being searched in the images. The default value is "apriltag_36h11", but we also support: "apriltag_16h5", "apriltag_25h7", "apriltag_25h9" or "apriltag_36h9"- Returns: While running, our tool will print the file it is currently working on, as well as the marker ids that have been detected in the respective image. Upon completion, the marker_detections.json file is written to the project path.
visual_marker_mapping:
--help: Shows a help text.--project-path: Path to the aforementioned project directory.--start-tag-id: The id of a tag that should be used as the origin of the coordinate system. It is suggested to use a tag that is located on the ground as one of the start tags. If you do not specify a start tag, the software will chose one itself.- Returns: Upon completion, the reconstruction.json file is written to the project path.
For visualization of the results in 3D, we also include a Python (2.7/3.0) script called "visualize_reconstruction.py". It is based on pygame, OpenGL, GLU, GLUT, numpy, and you may need to install the corresponding Python packages for your distribution in order to be able to run it.
The tool's only parameter is the path of the reconstruction.json file, that is being written by the visual_marker_mapping tool upon completion. The camera can be controlled using W, S, A, D. The mouse can be used to look around by holding the left mouse button. The camera speed can be increased by holding space.
We provide a test dataset, that you can use to test our tools. It is available here.
Use the following steps to perform the marker detection and 3D reconstruction (assuming you are in the root folder of this repository):
wget https://agas.uni-koblenz.de/data/datasets/visual_marker_mapping/calibration_room1.zip
unzip calibration_room1.zip
./build/bin/visual_marker_detection --project_path calibration_room1 --marker_width 0.1285 --marker_height 0.1295 --do_corner_refinement
./build/bin/visual_marker_mapping --project_path calibration_room1 --start_tag_id 230
If you want to visualize the results, simply run:
python3 visualize_reconstruction.py calibration_room1/reconstruction.json
Filename: camera_intrinsics.json
{
"fx" : "8.0752937867635346e+03",
"fy" : "8.0831676114192869e+03",
"cx" : "3.0163896805084278e+03",
"cy" : "1.9962896554785455e+03",
"distortion_coefficients" :
[
"-1.8618183262669760e-01", "3.7018092365577054e-01",
"-2.9390604003594177e-04", "4.1533180829908799e-04",
"5.7043887874185996e-02"
],
"vertical_resolution" : "4000",
"horizontal_resolution" : "6000"
}
Camera calibration model as defined here:
- fx, fy: Focal length in px
- cx, cy: Principal point in px
- distortion_coefficients: Exactly 5 radial distortion coefficients, that are in the order k_1, k_2, p_1, p_2, k_3. Not required coefficients can be set to 0.
Filename: marker_detections.json
{
"images":
[
{
"filename": "DSC05028.JPG",
"id": "0"
},
{
"filename": "DSC05076.JPG",
"id": "1"
},
[...]
],
"tags":
[
{
"id": "0",
"tag_type": "apriltag_36h11",
"width": "0.11650000000000001",
"height": "0.11650000000000001"
},
{
"id": "1",
"tag_type": "apriltag_36h11",
"width": "0.11650000000000001",
"height": "0.11650000000000001"
},
[...]
],
"tag_observations":
[
{
"image_id": "0",
"tag_id": "137",
"observations":
[
[
"4753.32275390625",
"572.0101318359375"
],
[
"5131.30810546875",
"568.25885009765625"
],
[
"5120.79541015625",
"201.8472900390625"
],
[
"4744.53369140625",
"205.41494750976562"
]
]
},
[...]
]
}
The individual marker corner observations are in the order: Lower left, Lower right, Upper right, Upper left
Filename: reconstruction.json
{
"reconstructed_tags":
[
{
"id": "0",
"type": "apriltag_36h11",
"width": "0.11650000000000001",
"height": "0.11650000000000001",
"rotation":
[
"0.99998768285276463",
"-0.0026651883409995361",
"-1.5875056612292212e-05",
"0.0041869633189374018"
],
"translation":
[
"0.0061616789246268563",
"0.37117687683311673",
"-0.0027071129933752556"
]
},
[...]
],
"reconstructed_cameras":
[
{
"id": "0",
"rotation":
[
"0.0071432235474879913",
"0.048540245468997004",
"0.050213553333055848",
"0.9975326651237153"
],
"translation":
[
"1.6566629838776454",
"-1.0628296493529241",
"-0.5984995791803972"
]
},
[...]
]
}
Occurring rotations are represented as a unit quaternion in the order w, x, y, z. Rotation and translation together define a pose that transforms points from marker/camera space to world space. The local coordinate systems are defined as follows:
- When looking at a marker, the x-axis goes to the right, y up, and z points out of the marker plane.
- A cameras x-axis points to the right, y-axis down, and the z-axis in viewing direction.
- The marker detector appears to have some issues with very high resolution camera images. As a temporary workaround, try downscaling the images. Don't forget to scale fx, fy, cx and cy in the camera_calibration.json file by the same amount. The marker detector is typically much more successful on smaller camera images.
- Frank Neuhaus (fneuhaus_AT_uni-koblenz.de)
- Stephan Manthe
- Lukas Debald
If you use our work, please cite us:
Practical Calibration of Actuated Multi-DOF Camera Systems. International Conference on Pattern Recognition Systems (ICPRS-17), 2017, IET. Juli 2017
Ed Olson, AprilTag: A robust and flexible visual fiducial system, Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 2011
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