1 / 26

Advanced Cubesat Imaging

Nathan Bossart, Joe Mayer, Bob Urberger. Advanced Cubesat Imaging. RASCAL ACIP. Team Introduction. http://acip.us Facilitator: Bob Urberger Computer Engineering majors Space Systems Research Lab. RASCAL Mission.

kineta
Télécharger la présentation

Advanced Cubesat Imaging

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Nathan Bossart, Joe Mayer, Bob Urberger Advanced Cubesat Imaging RASCAL ACIP

  2. Team Introduction • http://acip.us • Facilitator: Bob Urberger • Computer Engineering majors • Space Systems Research Lab

  3. RASCAL Mission • “Rascal is a two-spacecraft mission to demonstrate key technologies for proximity operations…” • “After the on-orbit checkout, one 3U spacecraft is released and passively drifts away.... After a suitable distance, the released spacecraft will activate its propulsion system and return to within a few meters of the base. The second spacecraft will be released and the process repeated...” RASCAL ACIP

  4. Imaging Payload • Awareness of Cubesat Environment • Computer Vision • Low-Level Processing • Object Detection • Distance Determination • High-Level Data • Navigation • Thruster Control RASCAL ACIP

  5. Functional Breakdown Unique Face Identifier Known Pattern Capture Images Raw Data Transfer Data Structured Data Process Images High-Level Data Output/Store Control Data RASCAL ACIP

  6. Modules LEDs Unique Face Identification Camera Capture Images Transfer Data Computational Hardware Process Images Output/Store Control Data RASCAL ACIP

  7. LEDs • Required to perform classification • Not enough detail visible for other features • Three approaches • Unique pattern of LEDs for each face • Unique combination of colors for each face • Both unique patterns and colors • Unique pattern works regardless of camera spectrum • Fails when face partially visible • Color combinations only work with visible spectrum cameras • Can classify cube corners as well as faces with well chosen color patterns RASCAL ACIP

  8. Camera • Potential Camera Choices: • FLIR Tau 640 • 640x480, 14-bit • Visible spectrum image sensor • 2-5MP - 16 to 24 bit color • Parallel data output from cameras • Component Requirements: • FLIR • PCB integration • Control signaling simple • Low resolution, monochromatic • 16.1 MB/s input data rate @ 30Hz • Visible spectrum • Requires lens fixture • Complex control signaling • High resolution, wide color range • 2MP with 24 bit color: • 57.6 MB/s input data rate @ 30Hz RASCAL ACIP

  9. Processing Hardware • Processing blocks in hardware • Caching and system control managed in software • Timing and Gate Consumption • Alternatives: • Pure software implementation • Pure hardware implementation RASCAL ACIP

  10. Imaging Functions Image Processing Distance Data Distance Detection Image Data Structure Objects In Frame Pre- Processing Object Detection Object Classification Image Edges RASCAL ACIP

  11. Preprocessing • Noise suppression • Color Conversion • Object enhancement • Image segmentation • Conversion and downsampling RASCAL ACIP

  12. Distance Detection • Identify depth from a single image • Monocular Cues • Relative size • Comparison of imaged objects to known shape scale at particular depth • Structured geometry identified should beeasy to identify scale • regular structure • Square or equilateral triangle RASCAL ACIP

  13. Distance Detection in RASCAL • Square LED pattern on spacecraft face • Critical point identification • Homography estimation • Projective transform • Point correspondence for scale • Hardware Domain • Parallel matrix multiplication RASCAL ACIP

  14. Object Detection • Identifying Objects in an Image • Region or Contour Based • Edge Detection • Relies heavily on Pre-processing (Columbia University) RASCAL ACIP

  15. Object Detection in RASCAL • Hardware Domain • Canny/Deriche • Sobel Operator • Constraints • Cubesat size • Environmental • Resolution (Columbia University) RASCAL ACIP

  16. Objection Classification • Post-object detection / image segmentation • Support vector machine (metric space classification) • Assign a class based upon pre-programmed control data RASCAL ACIP

  17. Object Classification in RASCAL • Completed with bare-metal software • ARM Assembly / C • Minimum distance principle (efficient) • Multi-tiered and/or multi-dimensional space from attributes given • Determine a number of attributes with significant differences between faces • Testing: expect a very high level (>95%) of correct classifications RASCAL ACIP

  18. Constraints of Object Classification • Must work with a variety of backgrounds (Earth, Moon, Sun, Space, etc.) • Ideally real time (bounded) and low latency • Updated at >=10 Hz • Must function with different sizes (patterns can vary from a few pels to larger than the frame) • Definitive discrimination functions with high reliability • Alternative algorithm: neural nets, fuzzy logic

  19. Output to Control • System will output calculated information about placement, attitude, distance, etc. • In the future, a separate team will construct a system to interpret data and convert to control signals/data • Since this is out of the scope of our project, the output format/setup is ultimately our choice RASCAL ACIP

  20. Functional Testing • Output Unique Pattern • Capture Images • Stream Images • Verify Control Signals • Transfer Data • Oscilloscope • Frame Buffer • Process Image • Software Verification • Hardware Verification • Output/Store Data • Buffer RASCAL ACIP

  21. System Testing • Camera integration • Hardware timing constraints • Block connectivity verification • Blocks signal each other as intended • Full pipeline simulation • Blocks interact as expected • Physical synthesis testing • Data produced from each frame RASCAL ACIP

  22. Timeline RASCAL ACIP

  23. Estimated Costs • Designed for very low budget and small amount of needed materials • Largely out of SSRL funding RASCAL ACIP

  24. Future Work for Integration • Thruster control system • Placement into spacecraft • Radiation, vibration, space-readiness • We will provide thorough documentation for future groups RASCAL ACIP

  25. Bibliography • http://cubesat.slu.edu/AstroLab/SLU-03__Rascal.html • Jan Erik Solem, Programming Computer Vision with Python. Creative Commons. • Dr. Ebel, Conversation • Dr. Fritts, Conversation • Dr. Mitchell, Conversation • Milan Sonka, Vaclav Hlavac, Roger Boyle, Image Processing, Analysis, and Machine Vision. CengageLearning; 3rd edition. • http://www.cs.columbia.edu/~jebara/htmlpapers/UTHESIS/node14.html RASCAL ACIP

  26. Questions? RASCAL ACIP

More Related