ruststartracker 0.2.2

Lightweight Python Star Tracker With Rust Backend

Lightweight Python Star Tracker With Rust Backend

Based on the methodology used in https://github.com/nasa/COTS-Star-Tracker, with following improvements:

• Reduced dependencies to opencv and numpy for lightweight usage in a Raspberry Pi.
• Reimplemented computationally expensive parts in rust. This includes most parts that are not image processing related.
• Added quadratic inter star angle index look up polynomial for faster triangle search.
• Added spatial index to look up neighboring stars.

Features:

• Attitude estimation from image and camera calibration parameters.
• Attitude estimation from list of star observation coordinates.
• Star catalog creation with temporal corrections.

Example

import ruststartracker

# Get catalog positions
catalog = ruststartracker.StarCatalog(max_magnitude=...)
star_catalog_vecs = catalog.normalized_positions(epoch=...)

# Define opencv camera parameters, see https://docs.opencv.org/4.x/dc/dbb/tutorial_py_calibration.html
camera_params = ruststartracker.CameraParameters(
    camera_matrix=...,
    cam_resolution=...,
    dist_coefs=...,
)

# Create StarTracker instance (reuse this)
st = ruststartracker.StarTracker(
    star_catalog_vecs,
    camera_params,
    max_inter_star_angle=...,
    inter_star_angle_tolerance=...,
    n_minimum_matches=...,
    timeout_secs=...,
)

# Obtain numpy array image
img = ...

# Find attitude from given image
result = st.process_image(img)

print(result)
# StarTrackerResult(quat=[-0.43977802991867065, -0.439766526222229, -0.4398997128009796, 0.6478340029716492], match_ids=[1435, 1272, 1140, 2035, 1070, 1438, 1338, 903, 260, 2141, 1771, 1727, 385, 1717, 2204, 2062, 1989, 1634, 708, 1357], n_matches=20, duration_s=0.0003700880042742938)

Installation

• Install with pip install ruststartracker (Currently only ARM/x86 Linux wheels available).

TODOs

• Improve error messages.
• Return more diagnostic data.

Attributions

Gaia Data

This project includes data from the European Space Agency (ESA) mission Gaia, processed by the Gaia Data Processing and Analysis Consortium (DPAC). Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the Gaia Multilateral Agreement.

Gaia DR3 data is © European Space Agency and is released under the Creative Commons Attribution 4.0 International License (CC BY 4.0).

Gaia Collaboration, Vallenari et al. (2022), A&A 674, A1. DOI: 10.1051/0004-6361/202243940