From Cognitive From Cognitive Spatial Mapping Spatial Mapping

to Robot Mappingto Robot Mapping

Margaret JefferiesMargaret JefferiesUniversity of WaikatoUniversity of Waikato

New ZealandNew ZealandHans-WissenschaftskollegHans-Wissenschaftskolleg

University of BremenUniversity of BremenGermanyGermany

Autonomous Mobile RobotsAutonomous Mobile Robots

Robots might not be taking over the world any time soon but they could soon rule the roost if most New Zealanders have their way.

More than two-thirds of New Zealanders would welcome robots to do chores around the house, according to a study of 750 people, commissioned by Honda. Most people wanted robots to help with housework, many wanted an extra mechanical hand with the washing up and some wanted a robot to mow the lawns.

Politicians and the All Blacks need to watch their backs – some respondents suggested robots should replace politicians and that a team of robots might fare better than the present rugby team.

Some people said they would even swap their partners for robots. Women were keener for a robotic partner, with 5.5 per cent saying they would like to switch, compared with just 3.3 per cent of men wanting to replace their partner.

Autonomous Mobile RobotsAutonomous Mobile RobotsMappingMapping

Robot computes its own map from it own Robot computes its own map from it own

experience of its environment with its experience of its environment with its

imperfect sensors and imperfect odometryimperfect sensors and imperfect odometry

What’s the problem?What’s the problem?

DemoDemo

Simultaneous Localisation and Simultaneous Localisation and Mapping (SLAM)Mapping (SLAM)

Robot needs to estimate its location at the same Robot needs to estimate its location at the same

time it is estimating its maptime it is estimating its map

The localisation problem

ApproachesApproaches

• Absolute Metric Mapping Absolute Metric Mapping

(Global metric mapping)(Global metric mapping)

• Topological MappingTopological Mapping

(Local metric maps)(Local metric maps)

Representation Representation Global MapsGlobal Maps

• Global evidence-grid approach Global evidence-grid approach

Global metric map

Global metric map

The Correspondence Problem (Closing the Cycle)

Topological RepresentationsTopological Representations

Topological RepresentationsTopological Representations

The Correspondence Problem (Closing the Cycle)

From Cognitive Spatial Mapping From Cognitive Spatial Mapping to Robot Mappingto Robot Mapping

Cognitive MapCognitive Map

An agents (human animal or robot’s) An agents (human animal or robot’s) memory of the spatial environmentmemory of the spatial environment

From Cognitive Spatial Mapping From Cognitive Spatial Mapping to Robot Mappingto Robot Mapping

• Draw inspiration from the way in which Draw inspiration from the way in which

humans and animals solve similar problemshumans and animals solve similar problems

• Study the way humans and animals solve Study the way humans and animals solve

similar spatial mapping problems (to robots)similar spatial mapping problems (to robots)

The Local SpaceThe Local Space

• The space that appears to enclose the viewerThe space that appears to enclose the viewer

• Initial notion of “where am I” Initial notion of “where am I”

• A container where objects are located and A container where objects are located and

where actions take placewhere actions take place

Bounded SpaceBounded Space

O’Keefe and Burgess Nature 1996 - hippocampus

Bounded spaceBounded space

• Russell Epstein and Nancy KanwisherRussell Epstein and Nancy Kanwisher– Nature (1998), Neuron (1999)Nature (1998), Neuron (1999)

• Parahippocampus encodes the layout of the Parahippocampus encodes the layout of the local space – the enclosed spacelocal space – the enclosed space

Bounded spaceBounded space

• Environmental Psychologists / GeographersEnvironmental Psychologists / Geographers

• 1980’s work of the Kaplans1980’s work of the Kaplans

• Stamps and Smith (2004)Stamps and Smith (2004)

The Local Space is The Local Space is GeometricGeometric

• Ken Cheng Ken Cheng – Cognition (1986)Cognition (1986)

• Margules and Gallistel Margules and Gallistel – Animal Learning and Behavior(1988)Animal Learning and Behavior(1988)

• Huttenlocher et alHuttenlocher et al– Cognitive Psychology (1979), (1994) Cognitive Psychology (1979), (1994)

• Hermer and Spelke Hermer and Spelke – Nature (1994), Cognition (1996)Nature (1994), Cognition (1996)

Exits are importantExits are important

• Evolutionary psychologistsEvolutionary psychologists– Kaplans (1980s)Kaplans (1980s)– Laslo et al “the Evolution of Cognitive Maps” Laslo et al “the Evolution of Cognitive Maps”

(1993)(1993)

• Environmental psychologistsEnvironmental psychologists– Herzog (2001 – 2004)Herzog (2001 – 2004)– Visual accessVisual access

The Local Mapping ApproachThe Local Mapping Approach

E1 E2

unknown

The Local Mapping ApproachThe Local Mapping Approach

Occlusion Map Local space representation

E1E2

E3

E4

Putting it all togetherPutting it all together

• The Theory of Siegel and White has The Theory of Siegel and White has

dominated thinking in this area since dominated thinking in this area since

it was first proposed in 1975it was first proposed in 1975

landmarkroute / topological survey

global metric

Most computational cognitive mapping approaches use all of these

Putting it all togetherPutting it all together

• The Theory of Siegel and White has The Theory of Siegel and White has

dominated thinking in this area since dominated thinking in this area since

it was first proposed in 1975it was first proposed in 1975

landmarkroute / topological survey

global metric

Most computational cognitive mapping approaches use all of these

E1

E2E3

E4

1

E1E2

E3

2

E1E2

E3

2

1

2

E2

E3

1

2

E2

E3

Local space

Topological Map Global Metric Map

DemoDemo

Detecting Cycles in a Global Metric MapDetecting Cycles in a Global Metric Map

• Need to figure out if a newly encountered Need to figure out if a newly encountered

local space is already in the topological local space is already in the topological

map map

• Need to account for the uncertainty in local Need to account for the uncertainty in local

spacespace– In particular occlusionIn particular occlusion

• Want to do it quicklyWant to do it quickly

Closing Cycles in a Topological Closing Cycles in a Topological MapMap

2D Landmarks2D Landmarks

Closing Cycles in a Topological Closing Cycles in a Topological MapMap

2D Landmarks2D Landmarks

• Find (eventually) a signature that identifies Find (eventually) a signature that identifies

the the local space from wherever it is the the local space from wherever it is

approachedapproached

• Learn what it is that makes each local Learn what it is that makes each local

space different from all the othersspace different from all the others

• Whenever we compute a new local space Whenever we compute a new local space

we match it against these signatureswe match it against these signatures

Signature learningSignature learning• A backprop Neural Network

• Feature selection

• Input values are discretised into intervals (200mm) and 45o

• Classification – Output values between 0 and 1 indicate the degree of similarity

12

3

4 5

6

71

2

3

4 5

6

7

11

2

34

5

6

7

8

9

10

11

Matches 2

Local Local

spacespace11 22 33 44 55 66 77 88 99 1010 1111

PredictionPrediction .78.78 .94.94 .89.89 .71.71 -.11-.11 .72.72 .18.18 .51.51 .34.34 .36.36 .04.04

2*1

2

34

5

6

7

8

9

1011

DisadvantageDisadvantage

• NN doesn’t tell us how the local spaces match just that NN doesn’t tell us how the local spaces match just that

they do.they do.

• Need to find the connectivityNeed to find the connectivity

ASRASR 11 22 33 44 55 66 77 88 99 1010

PredictionPrediction .46.46 .97.97 .91.91 .48.48 .64.64 .26.26 .57.57 .88.88 .15.15 .77.77

1

2

3

45

6

7

89

10

Best prediction is for 2

Should be 3

3D Visual Landmarks3D Visual Landmarks

ConclusionConclusion

• Recognising places they have been to Recognising places they have been to before is a hard problem for robotsbefore is a hard problem for robots

• There is no perfect solution!There is no perfect solution!

• Then there is the dynamics of the Then there is the dynamics of the environment to contend withenvironment to contend with