automatic-ar

This code is the reference implementation of the following paper:

H. Sarmadi, R. Muñoz-Salinas, M. A. Berbís and R. Medina-Carnicer, "Simultaneous Multi-View Camera Pose Estimation and Object Tracking With Squared Planar Markers," in IEEE Access, vol. 7, pp. 22927-22940, 2019.

Tested* Dependencies:

Required:

• CMake 3.1
• OpenCV 3.2.0
• Aruco 3.0.0 (Included in the project)

Optional:

• PCL 1.7.2 (for extra visualization)

*The project might work with older dependencies however it has not been tested. The code has been tested on Ubuntu 18.04.

Installing Dependencies on Ubuntu 18.04

Required:

sudo apt install build-essential cmake libopencv-dev

Optional:

sudo apt install libpcl-dev

How to compile the code

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/

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/ -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

Processing a sequence

1. Unzip the desired data set.

unzip box.zip

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

detect_markers box

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 box 0.05

Here 0.05 is the input size of each marker in meters.

Outputs

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

There are also *.yaml files that have the relative transformation between cameras, relative transformation between markers, and the relative transformation from the reference marker to the reference camera for each frame stored in the YAML format. You can use OpenCV's FileStorage class to read the data from the stored *.yaml files.

If you compile with PCL library you will also find *.cameras.pcd and *.markers.pcd which are point cloud based visualizations for cameras' configuration and markers' configuration respectively. These files could be viewed using pcl_viewer from the PCL library.

Tracking a sequence

For tracking the marker detection is done live so you do not need to do the detection in separate step. However, you need a processed sequence with the same camera and object configuration as your tracking sequence. Let's assume that we want to do tracking in the box_tracking sequence using the already processed box sequence (you can find both of them in the sample data sets). You just run:

track box_tracking box/final.solution

Visualization

If you compile with the PCL library you will have automatic 3D 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

Also the track app has a live overlay visualization in the runtime that does not need PCL.

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 for that camera.