Weekly Status Reports

This week, I upgraded the celestial positioning pipeline with ASTAP as the primary plate solver (1–2s vs 10–60s for astrometry.net) with automatic fallback when ASTAP can’t solve. I replaced attitude extraction with direct rotation matrix extraction. I alidated end-to-end by plate-solving a real iPhone star photo and a synthetic FITS image from NASA.

Next week I need to test the full pipeline on the Pi with the existing camera calibration and live IMU/mag data to get an actual lat/lon fix with ground truth comparison.

I am on track. The core math is all implemented and tested, camera calibration is done, and sensor data is validated. The remaining work is Pi integration and accuracy evaluation.

This week, our major progress points were in developing the hardware enclosure for all components and dealing with unforeseen corruption in the Linux install on the Raspberry Pi, which required re-installing the OS and re-configuring the device from scratch. We are in the process of 3D printing the enclosure, and are now set up for data collection this Tuesday when the forecasts show clear skies.

The designs for the enclosure are shown below with and without the top cover:

For validation, we plan to compare our system outputs against known references, including comparing IMU and visual attitude estimates and later comparing position estimates to GPS.

This week, the majority of my time was spent on preparing the interim demo. I brought up the camera calibration pipeline for display, and prepared slides on the software stack, calibration processes we developed, and the schedule for the next 3 weeks.

This week, the bulk of our preparation went into preparing the interim demo. We conducted further fine tuning of the complimentary filter, and brought up the camera calibration pipeline for demo’ing.

We continue to be blocked by lack of clear skies for testing, though forecasts indicate that this will come in the next 2 weeks.

This week, we made progress on both the IMU and visual pipelines. The IMU attitude estimation improved after calibration, and the complementary filter is now producing more stable results. On the visual side, we continued improving the consistency of the plate solving and attitude estimation.

We also attempted to collect new nighttime data, but weather conditions made it difficult to get usable images.

Overall, we are on track. Next, we plan to integrate the IMU and visual attitude estimates and begin testing the full system together.

This week, I focused on improving the robustness of the attitude estimation pipeline and validating its consistency across different image inputs. I refined the integration between the plate solver output and our calibrated camera model, which helped reduce inconsistencies in the computed orientations.

I need to begin connecting the attitude outputs to the next stage of the pipeline and figure out how to convert these orientations into preliminary latitude and longitude estimates. This is is critical for meeting our system accuracy goals.