In February 2025, Sam Klongo developed a real-time camera visualization node for the Stanford-AUV/RoboSub repository, focusing on enhancing field testing and debugging workflows. Using Python, ROS 2, and OpenCV, Sam built camera_viewer.py to subscribe to both raw and compressed image topics, convert them to OpenCV format, and display the streams in separate windows. The implementation included robust error handling for image conversion and ensured resources were properly released on shutdown. By providing direct visual feedback from the robot’s camera stack, this feature improved situational awareness and accelerated issue diagnosis, demonstrating a focused and technically sound approach to computer vision integration.