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 forMobile 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

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, …


This Talk

Motivation

SLAM

(Kalman filters)

Expectation

Maximization

Real Time

Hybrid

3D Mapping

with EM

Open

Problems


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

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

  • Concurrent Mapping and Localization (CML)

  • Simultaneous Localization and Mapping (SLAM)


Mapping: The Problem

  • Continuous variables

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

  • Multiple sources of noise

  • Simulation not acceptable


Milestone Approaches

Mataric 1990

Elfes/Moravec 1986

Kuipers et al 1991

Lu/Milios/Gutmann 1997


3D Mapping

Moravec et al, 2000

Konolige et al, 2001

Teller et al, 2000


Every state-of-the-art

mapping algorithm

is probabilistic.

Take-Home Message

Mapping is the

holy grail in

mobile robotics.


This Talk

Motivation

SLAM

(Kalman filters)

Expectation

Maximization

Real Time

Hybrid

3D Mapping

with EM

Open

Problems


Bayes Filters

x = state

t = time

z = measurement

u = control

 = constant

Special cases: HMMs

DBNs

POMDPs

Kalman filters

Condensation

...


Bayes Filters in Localization

[Simmons/Koenig 95]

[Kaelbling et al 96]

[Burgard, Fox, et al 96]


s = robot pose

m = map

t = time

 = constant

z = measurement

u = control

Localization:

Mapping?

Bayes Filters for Mapping


Kalman Filters (SLAM)

[Smith, Self, Cheeseman, 1990]


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


Large-Scale SLAM MappingCourtesy of John Leonard, MIT


SLAM: Limitations

  • Linear

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

  • Can’t solve data association problem


This Talk

Motivation

SLAM

(Kalman filters)

Expectation

Maximization

Real Time

Hybrid

3D Mapping

with EM

Open

Problems


E-Step: Localization

M-Step: Mapping with known poses

Unknown Data Association: EM

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


16 landmarks

15 landmarks

27 landmarks

17 landmarks

CMU’s Wean Hall (80 x 25 meters)


EM Mapping, Example (width 45 m)


EM Mapping: Limitations

  • Local Minima

  • Not Real-Time


This Talk

Motivation

SLAM

(Kalman filters)

Expectation

Maximization

Real Time

Hybrid

3D Mapping

with EM

Open

Problems


The Goal

Kalman filters:

real-time

No data association

EM:

data association

Not real-time

?


+

Incremental ML

Real-Time Approximation (ICRA paper)


Incremental ML: Not A Good Idea

mismatch

path

robot


+

Real-Time Approximation

Our ICRA Paper 


Real-Time Approximation

Yellow flashes:

artificially distorted map (30 deg, 50 cm)


Importance of Posterior Pose Estimate

With pose posterior

Without pose posterior


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

[Gutmann & Konolige, 00]


Accuracy: “The Tech” Museum, San Jose

2D Map, learned

CAD map


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

DARPA TMR Texas 7/99

(July. Texas. No air conditioning.

Req to dress up. Rattlesnakes)

DARPA TMR Maryland 7/00


3D Volumetric Mapping


3D Structure Mapping


3D Texture Mapping


Fine-Grained Structure:Can We Do Better?


This Talk

Motivation

SLAM

(Kalman filters)

Expectation

Maximization

Real Time

Hybrid

3D Mapping

with EM

Open

Problems


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

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

[Liu et al, ICML-01]


*

*

*

*

*

*

EM To The Rescue!

Game Over!


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

EM for

concurrent

localization

EM for

object

mapping


Underwater Mapping (with University of Sydney)

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


This Talk

Motivation

SLAM

(Kalman filters)

Expectation

Maximization

Real Time

Hybrid

3D Mapping

with EM

Open

Problems


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

  • 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

  • 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)


www.appliedautonomy.com


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