1. Seeker kick-off workshop LAAS involvement in the project Simon LACROIX & Bach Van PHAM Laboratoire d’Analyse et d’Architecture des Systèmes CNRS, Toulouse

2. Robotics @ LAAS 1979198519901995200020052011 2012 Personal robotics Humanoids Field robotics UAVs UGVs

3. Robotics @ LAAS About 80 people (20 academics, 40 PhDs, 20 postDocs, visitors, engineers) Organized in three research groups Wide spectrum of robotics-related research: Environment perception and modeling Navigation, localization, motion planning and control Natural, artificial and virtual motion Manipulation planning and control Autonomous decision making, temporal planning, learning Control architectures, embedded systems, robustness and fault tolerance Human-robot multi-modal and decisional interaction Multi-robot cooperation: decision, collaborative action … A constructive and integrative approach

4. LAAS people in Seeker Simon Lacroix (PhD 1995) Leads field robotics activities @ LAAS Research in perception, navigation and multi-robots systems Bach Van Pham (PhD 2010) ESA/Astrium funded thesis: “Pinpoint landing for a planetary probe” Vision / INS approach

5. LAAS people in Seeker Simon Lacroix (PhD 1995) Leads field robotics activities @ LAAS Research in perception, navigation and multi-robots systems Bach Van Pham (PhD 2010)

6. Presentation outline A short range navigation suite (Simon, 3 minutes) Absolute rover localisation (Bach Van, 8 minutes) Problem overview Existing solutions Foreseen solution A few questions (Simon, 1 minute)

7. Short range navigation “Reach a given waypoint” Waypoints defined by an overall itinerary planning step (two- stages navigation planning) Rather close (up to ten meters) Kind of obstacle avoidance scheme – akin to MER or CNES solutions (with slight differences) Decision Perception Action Traj. planning Stereovision (v,ω) commands DTM building Localisation Instantiated as:

8. Stereovision Classic approach (not up to date, algos from the 90s) Camera calibration Disparity image Correlation Static data Process Dynamic data Filtered disparity image 3D point cloud Filtering Triangulation Left Image Right Image Rectified right Image Rectified Left Image Rectification

9. DTM building on a regular Cartesian gridDTM: Ground rover case: Varying resolution Imprecision on the data uncertainties in the values

10. DTM building Simple solution: averaging of points height on a regular Cartesian gridDTM:

11. Trajectory “planning” Evaluation of a set of elementary trajectories (convolution of the terrain and robot models) To be adapted for the Seeker rover

12. Short range navigation 2001: 0.1m/s with stereovision 2011: 2.0m/s with Velodyne lidar

13. Presentation outline A short range navigation suite (Simon, 3 minutes) Absolute rover localisation (Bach Van, 8 minutes) Problem overview Existing solutions Foreseen solution A few questions (Simon, 1 minute)

14. Available data DTM of Holden Crater (1m/pixel) HIRISE Mars orbiter images 0.25m/pixel (here 0.5/pixel) + descent imagery ? (cf MSL)

15. Benefits of absolute Localization Build consistent global navigation maps Execute long range itineraries Reduce Human Intervention Improve overall mission performance (avoid dead-ends, reduce hazards, investigate site automatically) safer more hazardous

16. Skyline Matching [Cozman00] 1. Extract Skyline Signature for each DTM cell Signature of one cell: 1 elevation per azimuthElevation angle at one cell and one azimuth 2. Compare panorama skyline with DTM skylines Spirit Panorama - JPL

17. Skyline Matching [Cozman00] The goods: Use global features Robust to “lost-in-space” situations The bads: DTM must cover the horizon (!HIRISE) Localization error ≈ 5 times DTM resolution Do not use local features Require panorama image (p00xq000 pixels) High memory requirements: 1 cell needs 180(azimuth)*2(bytes- elevation) = 360 bytes

18. Surface Feature Matching [Hwangbo09] Rock width limits in [20,200] cm 1. Extract rocks from orbital data (e.g. HIRISE images using shadows)

19. Surface Feature Matching [Hwangbo09] 2. Extract rocks from stereo data Rock height > 22 cm

20. Surface Feature Matching [Hwangbo09] 3. Match rock patterns between rover and orbital data Black cross: ground rock Red circle: orbital rock Black triangle: rover position (updated = yellow)

21. Surface Feature Matching [Hwangbo09] The goods: Low memory requirement Proved to work with HIRISE images Useful for autonomous rock investigation (ProViScout) The bads: Does not work with site with no or small rocks Needs off-line rock detection

22. DTM Matching: Spin images [Vandapel06]

23. DTM Matching: Peak matching [Carle10]

24. DTM Matching The goods: Provide good position estimation (from 100 km error down to 30m) The bads: Memory depends on rover position uncertainty and DTM resolution (1 meter = 2 bytes(16 bit)): 11.44 MB for 2x3 km 2 area. Current experiments made with LIDAR data

25. Our current proposal Particle filtering Map-based approach Use DTM Matching likelihood function (fast)

26. Our current proposal 26 Global Map Scan 1

27. Our current proposal 27 Global Map Scan 1 1. Initialize particles with elevation correlation Hypothesis (for now): absolute heading known 100 best correlation positions are assigned as particles location (≈ absolute localization) One bad

28. Our current proposal 28 Global Map Scan 2 One bad 2. Update particles with new scan

29. Our current proposal 29 Global Map Scan 3 bad removed 3. Remove bad, generate new

30. Our current proposal 30 Global Map Scan 4 3. Remove bad, generate new

31. Illustration Not global localization wrt. an existing model, but incremental localization of scan(t) wrt. model(t-1)

32. Our current proposal The goods: Plenty The bads: None

33. Our current proposal The goods: All particles share the same local map (low memory requirement) Flexible: can combine other techniques (rock matching, spin- image) DTMs already required by navigation The bads: Processing time depends on the number of particles Risk of divergence (variance does not cover true error) TODOs: How to deal with orientation errors (5 o, 10 o, 20 o ?) – fast and easy signature? Better way to initialize first particles (using covariance value?) Better way to sample new particles Odometer error models (wheel, visual)? Ways to detect divergence?

34. Presentation outline A short range navigation suite (Simon, 3 minutes) Absolute rover localisation (Bach Van, 8 minutes) Problem overview Existing solutions Foreseen solution A few questions (Simon, 1 minute)

35. A few questions Wrt. these work: What chassis will be used in Seeker? Will Seeker follow a “stop / perceive-plan / move” scheme, or continuous motions ? How will software integration be tackled ? Need for initial “orbiter” data ASAP, on a terrain one can rapidly access On the final experimental site