Category: Rachel’s Status Reports

This week, I primarily focused on implementing and testing the plate solving functions to accurately extract the camera’s attitude. After successfully completing camera calibration, I began integrating the calibration parameters into the pipeline and testing how they affect the computed camera attitudes. I also started evaluating the ASTAP plate solver that Prof. Mukherjee recommended during our meeting last week, with the goal of improving the efficiency and runtime of our current solving pipeline.

In the coming week, I plan to make the attitude estimation functions more robust so that they can consistently produce reliable camera orientation estimates. Once the attitude estimation is stable, we will be able to move forward to the next stage of the pipeline, which involves estimating global longitude and latitude from the celestial observations.

This week I focused on the visual processing portion of our celestial positioning system. I worked on refining the visual pipeline architecture for our report, including the image capture, preprocessing, star detection, plate solving, WCS boresight extraction.

My progress is currently on schedule. The work I completed this week aligns with my role in image processing and vision, and it gives us a much clearer structure for implementing and validating the visual pipeline.

This week, I investigated optimal camera configurations for reliable star detection under nighttime conditions. I researched how exposure time, gain, and aperture impact signal-to-noise ratio, motion blur, and star visibility, especially given the UAV’s motion. I explored trade-offs between longer exposure, which gives brighter stars but increased motion blur, and shorter exposure, which has reduced blur but lower signal strength.

I am currently on schedule, as this week was intended to narrow down hardware and imaging parameter considerations before implementation. In the immediate next steps, I plan to begin testing different exposure and gain combinations on sample nighttime imagery and evaluate detection performance quantitatively.

This week, I focused on building and validating the core algorithmic pipeline for our celestial positioning system. I implemented star detection using preprocessing, adaptive thresholding, connected components, and centroid extraction on a stock starfield image. I then plate-solved the image using astrometry.net and integrated the resulting WCS file into our Python pipeline. The system achieved a mean angular error of approximately 0.0014 degrees, confirming that our detection and pose estimation steps are functioning accurately.

My progress is on schedule, as this week’s goal was to establish a working baseline registration and attitude estimation pipeline before implementing our own star identification method. Next week, I plan to replace the WCS-based labeling with a triangle-based star matching algorithm and validate that we can achieve similar accuracy independently.

This week, I focused on completing and refining the use case and user requirements for our design proposal. As part of this process, I explored the technical implications of our requirements, particularly calibration accuracy, environmental assumptions, and realistic localization error bounds. I also began foundational work for my role by doing initial research on star detection and tracking algorithms suitable for low-light conditions.

I am currently on schedule, as this week was intended for proposal refinement and background research. In the immediate next steps, I plan to prototype simple star detection and preprocessing pipelines using sample nighttime imagery. I also plan to further explore camera calibration workflows and imaging parameter tuning to prepare for early system-level testing.