MoCArU is a novel Motion Capture system based on ArUco developed by engineers at Tohoku University. The implementation given in this repository runs on ROS Noetic. In this tutorial, it is assumed that ROS Noetic is already installed. The following is a list of basic procedures necessary to operate MoCArU with image information from up to 10 cameras. By using lightweight camera stands, wireless communication, and a combination of odometry and ArUco recognition, MoCArU is specifically tailored for tracking swarm ground robot systems. It is a robust and cost-effective alternative to existing localization methods.

• Laboratory news - MoCArU, Human-Robot Informatics Laboratory of Tohoku University

• Paper - Published in 2023 IEEE Conference on Systems, Man, and Cybernetics (IEEE SMC) @Honolulu, HI, United States

ArUco markers must be placed on the objects you wish to track (the default number of objects is 2). If you don't have an idea what ArUco is, it is recommended to read this article. Moreover, you can generate ArUco markers for printing out at this website. Afterward, it is left to you to install cameras in your physical environment and have images streamed constantly using MJPEG.

• Ubuntu 20.04 (Consider Docker if you don't have one)
• ROS Noetic

These are other packages that are included as git-submodules: (1) (2) (3), and there are special instructions to modify certain codes in the packages prior to compiling them. If the Prerequisites are met already, please follow the instructions below, starting with cloning this repository at your preferred place.

git clone --recursive https://github.com/BRN-Hub/MoCArU.git

To install all dependencies, we recommend using rosdep by running the following command.

cd MoCArU/ros
rosdep install --from-paths src --ignore-src -r -y

It is recommended to set all files' permission in src folder to be executable. For example, you can use sudo chmod -R 777 src to grant full permission, but it is advised to check the security level as preferred in your environment.

There are some modifications needed - e.g., changing some commands and adding more files. In this tutorial, I will summarize the case of using Sony Spresense boards equipped with HDR cameras as the image source.

• Move files from requiredfiles/imagesourcelaunch to ros/src/image_source/launch/. Note that if you use other cameras, you are most likely needed to recalibrate the cameras to obtain a .yml file, using this calibration tool.
• Move files from requiredfiles/mjpeglaunch to ros/src/mjpeg_client/launch/. Currently, multicam_pic.launch is set to obtain images from 10 cameras from defined IP addresses, which you are totally free to modify.
• Move the file from requiredfiles/mjpegrviz to ros/src/mjpeg_client/rviz/.

All the mentioned file-moving steps can be accomplished by following these commands.

cd MoCArU
mv requiredfiles/imagesourcelaunch/* ros/src/image_source/launch/
mv requiredfiles/mjpeglaunch/* ros/src/mjpeg_client/launch/
mkdir -p ros/src/mjpeg_client/rviz && mv requiredfiles/mjpegrviz/* ros/src/mjpeg_client/rviz/

Source and compile.

cd ros
catkin_make
source devel/setup.bash

Steps to Start MoCArU: Receive Information from Camera Initiate the system to capture images sent from cameras over HTTP.

roslaunch mjpeg_client multicam_pic.launch

Run ArUco Recognition To recognize specific ArUco markers, execute:

roslaunch aruco_pose_estimation marker_estimation.launch

If you wish to modify the ArUco ID or the size of ArUco marker (in the unit of m), adjust it in the launch file above.

Run Camera Info This step is straightforward. Simply run:

roslaunch image_source camera_setting_info.launch

Calculate Robot Position Relative to ArUco ID0 You have two options to choose to proceed:

• To collect both average and Gaussian data combined from each camera:

roslaunch dist_from_tf info_combine_conv.launch

• To utilize Kalman data filtering (MoCArU), please use this launch file info_combine_kalman.launch instead. Either way, if your screen appears similar to the animation below, congratulations!

Outcome At this step, you will be able to check the position of your tracked objects as TF. We recommend visualizing it via RViz

(Optional) Try out our code with our Rosbag data We provide you with a bag file that contains sample image streaming, pose estimation, and velocity command data that we recorded during our test experiment. The bag file is bag/2rob_exp.bag, and it can be run with these bash scripts. We recommend using tmux to run each script below in a separate window, and you can observe the localization result via RViz.

roscore

roslaunch aruco_pose_estimation marker_estimation.launch

roslaunch dist_from_tf info_combine_kalman.launch

rosbag play MoCArU/data/2rob_exp.bag

We recommend to enable only frames turtle_kalman_t1 and turtle_kalman_t2 while disabling others.