
Worked on the itu-auv/auv-software repository to enhance reliability and maintainability of autonomous docking operations. Developed an ArUco marker-based pose estimation script in Python, enabling robust docking station localization using multiple markers and updated image topic subscriptions. Improved configuration management by introducing a YAML formatter in pre-commit hooks, which preserved whitespace and enhanced readability, supporting more reliable CI/CD workflows. Managed dependencies by forking the ultralytics_ros source, ensuring consistent builds and reducing integration issues. Leveraged skills in ROS, YAML, and DevOps to streamline deployment processes, reduce maintenance overhead, and enable safer, faster iteration for robotics perception and control systems.
December 2025 performance summary for itu-auv/auv-software. The team delivered targeted improvements focused on reliability, docking robustness, and maintainability. Key outcomes include forking the ultralytics_ros dependency source to our fork to ensure reliable builds, adding an ArUco-based docking station pose estimation script with multiple markers (including topic adjustments and cleanup of obsolete components), and enhancements to YAML formatting and configuration readability via a pre-commit formatter to improve CI/CD reliability. Major bugs fixed included cleanup of obsolete docking station/ArUco components and corrections to image topic subscriptions to align with the new pose estimation workflow. Overall impact: more stable deployments, improved docking accuracy, and reduced maintenance burden, enabling faster iteration and safer autonomous operations. Technologies/skills demonstrated: ROS-based perception and control, ArUco marker-based localization, dependency management via forks, pre-commit tooling, YAML formatting, and CI/CD workflow improvements.
December 2025 performance summary for itu-auv/auv-software. The team delivered targeted improvements focused on reliability, docking robustness, and maintainability. Key outcomes include forking the ultralytics_ros dependency source to our fork to ensure reliable builds, adding an ArUco-based docking station pose estimation script with multiple markers (including topic adjustments and cleanup of obsolete components), and enhancements to YAML formatting and configuration readability via a pre-commit formatter to improve CI/CD reliability. Major bugs fixed included cleanup of obsolete docking station/ArUco components and corrections to image topic subscriptions to align with the new pose estimation workflow. Overall impact: more stable deployments, improved docking accuracy, and reduced maintenance burden, enabling faster iteration and safer autonomous operations. Technologies/skills demonstrated: ROS-based perception and control, ArUco marker-based localization, dependency management via forks, pre-commit tooling, YAML formatting, and CI/CD workflow improvements.

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