Retinas

Retinas offers 3D world reconstruction and robot link localization system for modular robots using multiple AprilTags.

Requirements

Clone the repository:

git clone https://github.com/GitWyd/RM_Retinas.git

Navigate to the cloned directory:

cd RM_Retinas

Create a virtual environment using Python 3.8:

python3.8 -m venv venv_name

Activate the virtual environment:

source venv_name/bin/activate

Install the necessary dependencies:

pip install -r requirements.txt

AprilTag Detection: Detects AprilTags in real-time from a webcam feed.
Camera Calibration: Computes the calibration matrix for the camera.
Pose Estimation: Computes the 3D pose of each detected tag relative to the camera using OpenCV's solvePnP.
World Coordinate Transformation: Transforms detected tag poses into a global coordinate system, defined by certain "world tags".
Visualization: Displays the video feed with detected tags highlighted, showing tag ID, global pose, and reprojection error.
Reprojection Error Calculation: Calculates and visualizes reprojection errors for all detected tags.

Calibrate Your Camera:

python src/calibration.py

Once calibration is complete, launch the main application:

The spatial relationships of the predefined world tags are hardcoded into the script. Kindly modify if your world tag setups are different.
ELP 4KHDR01 USB Camera was used, but any 4K Camera would suffice.