Vision-based navigation around small bodies

Transcription

1 Astronet-II, International Final Conference Vision-based navigation around small bodies Pawel Kicman

2 VISION-BASED NAVIGATION IN SPACE Camera LOS (Line-of-sight) sensor Star-horizon measurements Apparent diameter estimation Range estimate Surface feature tracking Relative navigation Absolute navigation requires map/database Feature types Craters Robust point features (i.e. corners) Others 2015/06/19 Page 2

3 PAST MISSIONS TO SMALL BODIES NEAR-Shoemaker Hayabusa Hayabusa-2 Rosetta Dawn Vesta Ceres Honorable mention NASA Mars D&L System 2015/06/19 Page 3

4 PHOBOS SAMPLE RETURN MISSION OVERVIEW Sample return to Phobos First launch window in 2024 Only D&L (Descent and Landing) phase Landing accuracy requirement 50m 2015/06/19 Page 4

5 DESCENT AND LANDING ON PHOBOS REVIEW AND CHALLENGES Two main strategies Direct descent from QSO Descent from gateway Limitations Illumination constraints Eclipses Return time for the ground GO/NOGO command Ground based initialization is not possible due to time constraints Need for more autonomy and absolute navigation 2015/06/19 Page 5

6 VISUAL NAVIGATION ARCHITECTURE 2 main sensors Camera (20 FOV) Altimeter Relative Navigation Ground feauters tracking Support from altimeter Absolute Navigation Used only for initialization of the navigation filter Outside of main GNC chain Enhanced Relative Navigation (ERN) 2015/06/19 Page 6

7 Absolute Vision-based Navigation

8 ABSOLUTE NAVIGATION PROBLEM STATEMENT To calculate position of the spacecraft in bodyfixed reference frame Inputs Previously created map/database Image Low TRL 2015/06/19 Page 8

9 ABSOLUTE NAVIGATION FEATURE SELECTION Not enough distinctive craters on Phobos for robust navigation Point features as the best choice Corners Blobs Most important criteria Computational complexity Robustness to illumination changes view angle changes Descriptor Gradient of image intensity in the point neighbourhood Recognition of the same point in different conditions 2015/06/19 Page 9

10 ABSOLUTE NAVIGATION FEATURE SELECTION Most literature information only as initial guidance 6 combinations selected for evaluation on the representative data 2015/06/19 Page 10

11 ABSOLUTE NAVIGATION DATABASE GENERATION Set of images of the surface Varying view-angle Varying illumination conditions Known SC position Image-to-image matching Brute-Force matching Graph of interconections Connection between all features stored in efficient way 2015/06/19 Page 11

12 ABSOLUTE NAVIGATION DATABASE REFINEMENT Selecting the best landmarks Analysis of interconnected components Rejection of weakly connected features Rejection of outliers Calculating the average descriptor Triangulation of landmark position No model of the body required Limiting when precise SC position is unknown /06/19 Page 12

13 ABSOLUTE NAVIGATION POSITION CALCULATION Capture image Detect features Match image features to database Rejection of outliers Distance between projection of a landmark and a feature In the future robust filtering 2015/06/19 Page 13

14 ABSOLUTE NAVIGATION POSITION CALCULATION Solving PnP (Perspectiven-Point) problem At least 4 points needed The more points the better Efficient numerically EPnP algorithm Non-iterative No need for initial guess O(n) calculation time Refinement using Gauss- Newton algorithm 2015/06/19 Page 14

15 Experimental Results Images rendered in PANGU Images captured in platform-art laboratory

16 PANGU IMAGES PANGU Planet and Asteroid Natural Scene Generation Utility Rendered images Perfect ground truth Scaled Itokawa model Aproximate shape of Phobos in proximity of landing site Better resolution at low altitudes good model of Phobos not available Artificial artifacts due to texture wrapping 2015/06/19 Page 16

17 PANGU IMAGES INPUT Database generation Circle around landing site 10 km altitude 2 km radius 33 and 47 Sun elevation Test images 100 images 10 km above landing site Fluent change of Sun elevation /06/19 Page 17

18 PANGU IMAGES ACCURACY ANALYSIS Best results Software KLT + ORB SURF 7.68 and m Hardware KLT has much lower performance Feature type Position error [m] Match No. of landmarks in DB Max 3 rd quartile Median Min percentage KLT_H+ORB % KLT_H+BRISK % KLT_H+SIFT % KLT_H+SURF % SURF % KLT_S+ORB % 2015/06/19 Page 18

19 PLATFORM-ART IMAGES Platform-art optical laboratory for simulation of proximity operations Moving camera Simulated movement of the Sun Scale factor [-] Careful calibration necessary! Evaluation of algorithm on more realistic images 2015/06/19 Page 19

20 PLATFORM-ART IMAGES - INPUT Database generation 2 circles above landing site 5 km altitude 1 km radius 33 and 47 Sun elevation Test images 100 images 5 km above landing site Fluent change of Sun elevation /06/19 Page 20

21 PLATFORM-ART IMAGES ACCURACY Best results Hardware KLT + ORB, but has many outliers! SURF m accuracy Feature type Position error [m] Match No. of landmarks in DB Max 3 rd quartile Median Min percentage KLT_H+ORB % KLT_H+BRISK % KLT_H+SIFT % KLT_H+SURF % SURF % KLT_S+ORB % 2015/06/19 Page 21

22 CONCLUSIONS AND FURTHER WORK Initially satisfactory accuracy Succesful recognition of landmarks under changes of illumination and view-angle Evaluation on both rendered and real images Very sensitive to outliers More accurate positions of landmarks Incorporate landmark position uncertainty into calculations Robust outlier rejection Bug in hardware KLT implementation 2015/06/19 Page 22

23 Thank you Pawel Kicman GMV, Marie Sklodowska-Curie fellow