seeker kick-off workshop laas involvement in the project simon lacroix bach van pham laboratoire...
DESCRIPTION
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 approachTRANSCRIPT
Seeker kick-off workshop
LAAS involvement in the project
Simon LACROIX & Bach Van PHAMLaboratoire d’Analyse et
d’Architecture des SystèmesCNRS, Toulouse
Robotics @ LAAS
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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
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
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)
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)
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
PerceptionAction
Traj. planning
Stereovision(v,ω) commands
DTM building
Localisation
Instantiated as:
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 ImageRectification
Rectification
DTM building
),( yxfz on a regular Cartesian gridDTM:
Ground rover case:
• Varying resolution
• Imprecision on the data uncertainties in the values
DTM building
Simple solution: averaging of points height
),( yxfz on a regular Cartesian gridDTM:
Trajectory “planning”
• Evaluation of a set of elementary trajectories (convolution of the terrain and robot models)
To be adapted for the Seeker rover
Short range navigation
2001: 0.1m/s with stereovision 2011: 2.0m/s with Velodyne lidar
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)
Available data
DTM of Holden Crater(1m/pixel)
HIRISE Mars orbiter images0.25m/pixel (here 0.5/pixel)
+ descent imagery ?(cf MSL)
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
Skyline Matching [Cozman00]1. Extract Skyline Signature for each DTM cell
Signature of one cell: 1 elevation per azimuth Elevation angle at one cell and one azimuth
2. Compare panorama skyline with DTM skylines
Spirit Panorama - JPL
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
Surface Feature Matching [Hwangbo09]
Rock width limits in [20,200] cm
1. Extract rocks from orbital data (e.g. HIRISE images using shadows)
Surface Feature Matching [Hwangbo09]
2. Extract rocks from stereo data
Rock height > 22 cm
Surface Feature Matching [Hwangbo09]
3. Match rock patterns between rover and orbital data
Black cross: ground rockRed circle: orbital rockBlack triangle: rover position (updated = yellow)
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
DTM Matching: Spin images [Vandapel06]
DTM Matching: Peak matching [Carle10]
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 km2 area.• Current experiments made with LIDAR data
Our current proposal
Particle filtering Map-based approach • Use DTM Matching likelihood function (fast)
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Our current proposal
Global MapScan 1
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Our current proposal
Global MapScan 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
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Our current proposal
Global MapScan 2
One bad
2. Update particles with new scan
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Our current proposal
Global MapScan 3
badremoved
3. Remove bad, generate new
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Our current proposal
Global MapScan 4
3. Remove bad, generate new
Illustration
Not global localization wrt. an existing model, but incremental localization of scan(t) wrt. model(t-1)
Our current proposal The goods:
• Plenty• The bads:
• None
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 (5o, 10o, 20o?) – 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?
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)
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