Probabilistic algorithms for mobile robot mapping
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Probabilistic Algorithms for Mobile Robot Mapping. Sebastian Thrun Carnegie Mellon & Stanford Wolfram Burgard University of Freiburg and Dieter Fox University of Washington. Based on the paper A Real-Time Algorithm for Mobile Robot Mapping With Applications to Multi-Robot and 3D Mapping

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Probabilistic Algorithms for Mobile Robot Mapping

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Probabilistic algorithms for mobile robot mapping

Probabilistic Algorithms forMobile Robot Mapping

Sebastian Thrun

Carnegie Mellon & Stanford

Wolfram Burgard

University of Freiburg

and Dieter Fox

University of Washington


Probabilistic algorithms for mobile robot mapping

Based on the paper

A Real-Time Algorithm for Mobile Robot Mapping

With Applications to Multi-Robot and 3D Mapping

Best paper award at 2000 IEEE International Conference on Robotics

and Automation (~1,100 submissions)

Sponsored by DARPA (TMR-J.Blitch, MARS-D.Gage, MICA-S.Heise)

and NSF (ITR(2), CAREER-E.Glinert, IIS-V.Lumelsky)

Other contributors: Yufeng Liu, Rosemary Emery, Deepayan Charkrabarti, Frank Dellaert, Michael

Montemerlo, Reid Simmons, Hugh Durrant-Whyte, Somajyoti Majnuder, Nick Roy, Joelle Pineau, …


Probabilistic algorithms for mobile robot mapping

This Talk

Motivation

SLAM

(Kalman filters)

Expectation

Maximization

Real Time

Hybrid

3D Mapping

with EM

Open

Problems


Museum tour guide robots

Museum Tour-Guide Robots

With: Greg Armstrong, Michael Beetz, Maren Benewitz, Wolfram Burgard, Armin Cremers, Frank Dellaert, Dieter Fox, Dirk Haenel, Chuck Rosenberg, Nicholas Roy, Jamie Schulte, Dirk Schulz


The nursebot initiative

The Nursebot Initiative

With: Greg Armstrong, Greg Baltus, Jacqueline Dunbar-Jacob, Jennifer Goetz, Sara Kiesler, Judith Matthews, Colleen McCarthy, Michael Montemerlo, Joelle Pineau, Martha Pollack, Nicholas Roy, Jamie Schulte


Mapping the problem

Mapping: The Problem

  • Concurrent Mapping and Localization (CML)

  • Simultaneous Localization and Mapping (SLAM)


Mapping the problem1

Mapping: The Problem

  • Continuous variables

  • High-dimensional (eg, 1,000,000+ dimensions)

  • Multiple sources of noise

  • Simulation not acceptable


Milestone approaches

Milestone Approaches

Mataric 1990

Elfes/Moravec 1986

Kuipers et al 1991

Lu/Milios/Gutmann 1997


3d mapping

3D Mapping

Moravec et al, 2000

Konolige et al, 2001

Teller et al, 2000


Take home message

Every state-of-the-art

mapping algorithm

is probabilistic.

Take-Home Message

Mapping is the

holy grail in

mobile robotics.


Probabilistic algorithms for mobile robot mapping

This Talk

Motivation

SLAM

(Kalman filters)

Expectation

Maximization

Real Time

Hybrid

3D Mapping

with EM

Open

Problems


Bayes filters

Bayes Filters

x = state

t = time

z = measurement

u = control

 = constant

Special cases: HMMs

DBNs

POMDPs

Kalman filters

Condensation

...


Bayes filters in localization

Bayes Filters in Localization

[Simmons/Koenig 95]

[Kaelbling et al 96]

[Burgard, Fox, et al 96]


Bayes filters for mapping

s = robot pose

m = map

t = time

 = constant

z = measurement

u = control

Localization:

Mapping?

Bayes Filters for Mapping


Kalman filters slam

Kalman Filters (SLAM)

[Smith, Self, Cheeseman, 1990]


Underwater mapping with slam courtesy of hugh durrant whyte univ of sydney

Underwater Mapping with SLAMCourtesy of Hugh Durrant-Whyte, Univ of Sydney


Large scale slam mapping courtesy of john leonard mit

Large-Scale SLAM MappingCourtesy of John Leonard, MIT


Slam limitations

SLAM: Limitations

  • Linear

  • Scaling: O(N4) in number of features in map

  • Can’t solve data association problem


Probabilistic algorithms for mobile robot mapping

This Talk

Motivation

SLAM

(Kalman filters)

Expectation

Maximization

Real Time

Hybrid

3D Mapping

with EM

Open

Problems


Unknown data association em

E-Step: Localization

M-Step: Mapping with known poses

Unknown Data Association: EM

[Dempster et al, 77] [Thrun et al, 1998] [Shatkay/Kaelbling 1997]


Cmu s wean hall 80 x 25 meters

16 landmarks

15 landmarks

27 landmarks

17 landmarks

CMU’s Wean Hall (80 x 25 meters)


Em mapping example width 45 m

EM Mapping, Example (width 45 m)


Em mapping limitations

EM Mapping: Limitations

  • Local Minima

  • Not Real-Time


Probabilistic algorithms for mobile robot mapping

This Talk

Motivation

SLAM

(Kalman filters)

Expectation

Maximization

Real Time

Hybrid

3D Mapping

with EM

Open

Problems


The goal

The Goal

Kalman filters:

real-time

No data association

EM:

data association

Not real-time

?


Real time approximation icra paper

+

Incremental ML

Real-Time Approximation (ICRA paper)


Incremental ml not a good idea

Incremental ML: Not A Good Idea

mismatch

path

robot


Real time approximation

+

Real-Time Approximation

Our ICRA Paper 


Real time approximation1

Real-Time Approximation

Yellow flashes:

artificially distorted map (30 deg, 50 cm)


Importance of posterior pose estimate

Importance of Posterior Pose Estimate

With pose posterior

Without pose posterior


Online mapping with posterior courtesy of kurt konolige sri darpa tmr

Online Mapping with PosteriorCourtesy of Kurt Konolige, SRI, DARPA-TMR

[Gutmann & Konolige, 00]


Accuracy the tech museum san jose

Accuracy: “The Tech” Museum, San Jose

2D Map, learned

CAD map


Multi robot mapping

Multi-Robot Mapping

Cascaded architecture

  • Every module maximizes likelihood

  • Pre-aligned scans are passed up in hierarchy

map

map

Pre-aligned scans

Aligned map

map

map

map


Multi robot exploration

Multi-Robot Exploration

DARPA TMR Texas 7/99

(July. Texas. No air conditioning.

Req to dress up. Rattlesnakes)

DARPA TMR Maryland 7/00


3d volumetric mapping

3D Volumetric Mapping


3d structure mapping

3D Structure Mapping


3d texture mapping

3D Texture Mapping


Fine grained structure can we do better

Fine-Grained Structure:Can We Do Better?


Probabilistic algorithms for mobile robot mapping

This Talk

Motivation

SLAM

(Kalman filters)

Expectation

Maximization

Real Time

Hybrid

3D Mapping

with EM

Open

Problems


Multi planar 3d mapping

Multi-Planar 3D Mapping

Idea: Exploit fact that buildings posses many planar surfaces

  • Compact models

  • High Accuracy

  • Objects instead of pixels


3d multi plane mapping problem

3D Multi-Plane Mapping Problem

Entails five problems

  • Generative model with priors: Not all of the world is planar

  • Parameter estimation: Location and angle of planar surfaces unknown

  • Outlier identification: Not all measurements correspond to planar surfaces (other objects, noise)

  • Correspondence: Different measurements correspond to different planar surfaces

  • Model selection: Number of planar surfaces unknown


Expected log likelihood function

Expected Log-Likelihood Function

[Liu et al, ICML-01]


Em to the rescue

*

*

*

*

*

*

EM To The Rescue!

Game Over!


Results

Results

With EM

(95% of data explained by 7 surfaces)

Without EM

error

With: Deepayan Chakrabarti, Rosemary Emery, Yufeng Liu, Wolfram Burgard, ICML-01


The obvious next step

The Obvious Next Step

EM for

concurrent

localization

EM for

object

mapping


Underwater mapping with university of sydney

Underwater Mapping (with University of Sydney)

With: Hugh Durrant-Whyte, Somajyoti Majunder, Marc de Battista, Steve Scheding


Probabilistic algorithms for mobile robot mapping

This Talk

Motivation

SLAM

(Kalman filters)

Expectation

Maximization

Real Time

Hybrid

3D Mapping

with EM

Open

Problems


Take home message1

Take-Home Message

Mapping is the

holy grail in

mobile robotics.

Every state-of-the-art

mapping algorithm

is probabilistic.

Sebastian has

one cool animation!


Open problems

Open Problems

  • 2D Indoor mapping and exploration

  • 3D mapping (real-time, multi-robot)

  • Object mapping (desks, chairs, doors, …)

  • Outdoors, underwater, planetary

  • Dynamic environments (people, retail stores)

  • Full posterior with data association (real-time, optimal)


Open problems con t

Open Problems, con’t

  • Mapping, localization

  • Control/Planning under uncertainty

  • Integration of symbolic making

  • Human robot interaction

    Literature Pointers:

  • “Robotic Mapping” at www.thrun.org

  • “Probabilistic Robotics” AI Magazine 21(4)


Probabilistic algorithms for mobile robot mapping

www.appliedautonomy.com


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