seeker kick-off workshop laas involvement in the project simon lacroix bach van pham laboratoire...

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

28

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