RoboDine is a vision and robotics project that trained a YOLO pose model with data preprocessing and augmentation to reduce pre-grasp pose-estimation errors for a robot arm with a monocular 2D camera. It improved 6-DoF pose-estimation accuracy by 14%, implemented Hough Circles and DBSCAN-based logic for foreign-object scenarios in the loading area, and used GitHub, Jira, and Confluence in an Agile workflow.
Highlights
- Trained a YOLO pose model with data preprocessing and augmentation to reduce pre-grasp pose-estimation errors for a robot arm with a monocular 2D camera, improving 6-DoF pose-estimation accuracy by 14%.
- Implemented Hough Circles and DBSCAN-based logic to prepare for scenarios where foreign objects cause issues in the loading area.
- Collaborated through GitHub, Jira, and Confluence while following an Agile development process.
Demo Videos
Repository
git clone https://github.com/addinedu-roscamp-4th/roscamp-repo-2.git
cd roscamp-repo-2