chunyang-zhang/automatic-ar-1

automatic-ar

Tested* prerequisites:

• CMake 2.8

• Aruco 3.0.0 (Included in the project)

• OpenCV 3.2.0

• PCL 1.7.2 (Optional, for point cloud I/O and visualization)

*The project might work with older dependencies however we have not tested.

Hot to compile

Like a normal cmake project make a build library:

mkdir automatic-ar-build

change the current directory to the build directory:

cd automatic-ar-build

run cmake to configure the project and generate the makefiles:

cmake ../automatic-ar/trunk/

in case cmake does not find a library you have automatically, you can manually give cmake the path to where the library's cmake configuration file exists. For example

cmake ../automatic-ar/trunk/ -DOpenCV_DIR=~/local/share/OpenCV

Finally run make to build the binaries

make

You will find the executables to work with in the apps directory.

Sample data

You can download sample data sets from here.

Usage

1. Unzip the desired data set.

unzip pentagonal.zip

2. Do the marker detection by giving the path of the data set to detect_markers:

detect_markers pentagonal

After unzipping you will find a file name aruco.detections in the data folder path.

3. Apply the algorithm by providing the data folder path and the physical size of the marker to find_solution:

find_solution pentagonal 0.04

Here 0.04 is the size of each marker in meters.

The output of find_solutions includes several files. Files with the name format 'initial*' store information when the solution after initialization and before optimization. The files with their names 'final*' store information resulted after doing the optimization.

Visualization

If you compile with the PCL library you will have automatic visualization when you run find_solution. However, if not, you can still visualize the solution using the overlay app:

overlay pentagonal

You can also save overlayed visualization in a video in the dataset folder by using an extra option:

overlay pentagonal -save-video

Dataset format

Each dataset is defined within a folder. In that folder each camera has a directory with its index as its name. In the cameras folders there is a "calib.xml" file specifying the camera matrix, distortion coefficients and the image size in OpenCV calibration output style.