Stanford CS223B Computer Vision, Winter 2005 Lecture 13: Learning Large Environment Models Sebastian Thrun, Stanford Rick Szeliski, Microsoft Hendrik

Stanford CS223B Computer Vision, Winter 2005

Lecture 13: Learning Large Environment Models

Sebastian Thrun, Stanford

Rick Szeliski, Microsoft

Hendrik Dahlkamp and Dan Morris, Stanford

Sebastian Thrun Stanford University CS223B Computer Vision

The SLAM Problem

Simultaneous Localization and Mapping Same as: Structure from Motion

– Large environments– Massive occlusion– Hard correspondence problems

Konolige et al, 2001 Teller et al, 2000


Example


Mining Accidents…

Somerset County, Quecreek Mine, July, 2002


Mining Accidents…

Somerset County, Quecreek Mine, July, 2002


Mine Subsidence Problems

Source: Bureau of Abandoned Mine Reclamation


Mine Subsidence Problems

Source: Bureau of Abandoned Mine Reclamation


Course: CMU RI 16-894

with Red Whittaker, Scott Thayer, 10+ students


The Groundhog Robot

with Red Whittaker, Scott Thayer, 10+ students


Groundhog: Burgesttown, PA






100 Feet In!


Operator Control Unit


October 27 is Groundhog Day!


The Only Mine Map


The Basic Problem

Mapping Mines– Very large environments,

many cycles

– Volumes, centimeter accuracy

– Real-time

– Autonomous (no communication)

Is instance of: SLAM Problem (Simultaneous Localization and Mapping)

– Hundreds of millions of features

– Massive data association


The Problem: SLAM

Mapping Mines– Very large environments,

many cycles

– Volumes, centimeter accuracy

– Real-time

– Autonomous (no communication)

Is instance of: SLAM Problem (Simultaneous Localization and Mapping)

– Hundreds of millions of features

– Massive data association

SLAM with Known Map (Localization)

Restriction: Known data association (for now)


The Problem: SLAM

SLAM with Known Locations (Mapping) SLAM with Known Map (Localization)



The Problem: SLAM

SLAM with Known Locations (Mapping) S L A M


Limit


EKF Solution [Smith/Cheeseman 1986]

S L A M

tttT

ttt mmmp 1

2

1exp)(

t covariance

mt robot pose and features

t expectation

Extended Kalman Filter


Limit


EKF Solution [Smith/Cheeseman 1986]

tttT

ttt mmmp 1

2

1exp)(

t covariance

mt robot pose and features

t expectation



2

2

2

2

2

2

2

1

11

21

21

21

21

2222221

1111211

,),|(

yxlll

yyxyylylyl

xxyxxlxlxl

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Nttt

N

N

N

NNNNNN

N

N

y

x

l

l

l

uzmp


Classical Solution [Smith/Cheeseman 1986]


tttT

ttt mmmp 1

2

1exp)(


Evolution Robotics


Maps Acquired by Groundhog25

0 m

eter

s


Maps Acquired by Groundhog

Bruceton Research Mine

250

met

ers


Summary SLAM

Is a Hybrid Tracking Problem– Camera pose (robot)– Large number of environmental features– Large number of data association variables

Solution Kalman Filter (very high dimensional)

Documents

Stanford CS223B Computer Vision, Winter 2005 Lecture 13: Learning Large Environment Models Sebastian Thrun, Stanford Rick Szeliski, Microsoft Hendrik