Hoku (Lost-in-Space Star Identification for Spacecraft)

Note: this project is no longer being maintained.

Overview

Ancient mariners could look up the night sky, point out what stars they were looking at, and navigate across the globe with precision. Star identification algorithm refers to a computational approach to pointing out which stars are in the sky. Given an image of the sky, star identification is matching the bright spots in an image, to stars in an astronomical catalog. The device that performs these computations is the star tracker, much like the navigators on the ship. Lost-in-space refers to an additional constraint on the problem: the absence of knowing where we took the picture and how we pointed the camera.

This repository holds research toward the analysis of various lost-in-space star identification procedures for spacecraft. This includes a study of feature uniqueness, permutation order, candidate reduction, and identification under the introduction of various noise. The process of identifying blobs in an image, constructing the image coordinate system, and efficiently querying static databases is not addressed here, but there does exist some rudimentary programs to execute the attitude determination process end-to-end.

Getting Started (Quick Start)

This repository requires the following:

1. python3 (with numpy, matplotlib, opencv). Used for the data analysis and visualization.
2. CMake (2.8.10) or above. Used to manage and build the C++ code here.
3. git or some Git client. Used to clone this repository, and to grab GoogleTest for testing.
4. C++ build tools (make 4.1, gcc 5.4, g++ 5.4).

To get started, clone this repository:

git clone https://github.com/glennga/hoku.git

Generate the executables. The output will reside in the bin folder.

# Create the Makefiles.
cd hoku/build
cmake -G"Unix Makefiles" ..

# Build the executables.
cd src
make -j8 install

Run the setup script to construct the Nibble database. Modify the generation of this database through hoku/hoku.cfg.

./hoku/hoku.setup

Running Experiments

There exist four experiments in this research:

1. Feature Uniqueness (query)
2. Candidate Reduction (reduction)
3. Identification (identification)
4. Overlay (overlay)

There exist six different identification methods implemented here:

1. Gottlieb's Angle Method (angle)
2. Liebe's Dot Angle Method (dot)
3. Cole and Crassidus's Spherical Triangle Method (sphere)
4. Cole and Crassidus's Planar Triangle Method (plane)
5. Mortari's Pyramid Method (pyramid)
6. Toloei's Composite Pyramid Method (composite)

To run experiments for all methods, use the hoku/hoku.run script. The results will be logged in the Lumberjack database (lumberjack.db) by default, stored in tables according to the experiments and grouped by the experiment timestamp.

Google Test Generation

Google Test is attached as a Git Submodule. Run the following commands to get Google Test in this repository.

cd hoku
git submodule init
git submodule update

Generate the test runner. The output will reside in the bin folder.

# Create the Makefiles.
cd hoku/build
cmake -G"Unix Makefiles" -DBUILD_TEST=ON ..

# Build the test runner.
cd test
make -j8 install

Execute the test runner. By default, this runs all of tests in the tests folder. If desired, you can restrict the tests to a certain pattern using the --gtest_filter option. Note that all classes are sorted into their own test suite. More information on this argument can be found here.

cd hoku/bin

# Run all of the tests in the 'tests' folder.
./PerformT

# Run only the Benchmark tests.
./PerformT --gtest_filter=Benchmark*