Experience Mapping: Producing Spatially Continuous EnvironmentRepresentations using RatSLAM

Michael Milford, David Prasser, and Gordon Wyeth

School of Information Technology and Electrical Engineering

The University of Queensland

{milford, prasserd, wyeth}@itee.uq.edu.au

Abstract

RatSLAM is a system for vision-based Simul-taneous Localisation and Mapping (SLAM)inspired by models of the rodent hippocampus.The system can produce stable representationsof large complex environments during robotexperiments in both indoor and outdoor en-vironments. These representations are bothtopological and metric in nature, and caninvolve multiple representations of the sameplace as well as discontinuities. In this paper wedescribe a new technique known as experiencemapping that can be used on-line with theRatSLAM system to produce world representa-tions known as experience maps. These mapsgroup together multiple place representationsand are spatially continuous. A number ofexperiments have been conducted in simulationand a real world office environment. Theseexperiments demonstrate the high degree towhich experience maps are representative of thespatial arrangement of the environment.

1 Introduction

In previous work we have shown a system called Rat-SLAM performing on-line Simultaneous Localisation andMapping (SLAM) in a range of unmodified indoor[Milford et al., 2004] and outdoor [Prasser et al., 2005]

environments using only vision and wheel encoder in-formation. More recently we have also demonstrated anew module known as goal memory that can use thedistributed world representations RatSLAM learns toperform efficient goal orientated navigation. Resultswere presented showing the system performing visionbased SLAM on-line and immediately using its learntrepresentations to navigate directly to designated goallocations [Milford et al., 2005].1

1This research is sponsored in part by an AustralianResearch Council grant.

In this paper we describe a new technique known asexperience mapping that builds on top of the RatSLAMsystem to create world representations that are spatiallycontinuous — local areas of the map accurately portraythe Cartesian properties of the mapped area in theenvironment. This technique has several fundamentaladvantages over the original RatSLAM representations.As environments become larger and more complex,the experience map remains continuous, whilst theRatSLAM representation that underlies it becomes in-creasingly topological and discontinuous. Because itis made up of experiences that incorporate both poseand visual information, the likelihood of hash collisionsis drastically reduced. Lastly, the experience mapgathers multiple representations of the same places intooverlapping areas in the map.

Section 2 discusses the current SLAM methodologiesand highlights the weaknesses in the field. In Section 3we describe the RatSLAM structure and dynamics anddetail some of the limitations that were highlighted byattempts to move to longer experiments in larger, morecomplex environments. Section 4 describes the imple-mentation of the experience mapping technique. Section5 describes the setup of the simulation experiments andpresents the results. In Section 6 the simulation resultsare validated by results from a real world experiment.Discussion of the results and implications follows inSection 7 before the paper is concluded in Section 8.

2 Background

Simultaneous Localisation and Mapping is one of themost challenging problems in mobile robotics. Over thelast 10 years the exponential increase in computationalpower available to roboticists has allowed theoreticalsolutions to the problem to finally be implemented inlarge scale real world experiments. Impressive resultshave been generated using a number of techniques, suchas Kalman Filters and Expectation Maximisation ap-proaches. However, even with the computer capabilitiesof today, compromises are invariably made in one or

Figure 1: High resolution occupancy map of the Univer-sity of Freiburg Campus [Grisetti et al., 2005].

more aspects of these methods. For instance, in order tomap a large outdoor environment, some methods resortto computationally intensive off-line procedures whichoccur after the actual experiment. Alternative tech-niques incorporate assumptions about the environmentthat greatly simplify the mathematics and reduce thecomputational processing required to solve the SLAMproblem.

Furthermore a large fraction of the most impressiveresults in the field have been generated from techniquesrequiring high fidelity sensors, most commonly laserrange scanners. From a mathematical perspective thesesensors are ideal because they provide plenty of highprecision data. But when one looks at animals andhumans, it is quite clear that there are, “other waysto do it.” The use of laser based techniques alsohas a significant consequence — the data processingrequirements for the high resolution maps generated(Figure 1) is significant and often forces the computationoff-line. Landmark based techniques avoid some of thiscomputational problem but require either environmentalassumptions about the types of landmarks or a veryaccurate and robust general feature recognition system.

One of the biggest problems with many solutions tothe SLAM problem is the absence of any well integratedtask performance system. After all, the reason livingthings explore and map their environments is so thatthey can efficiently move around in it, whether an antor a human. There has been some limited presentationin the literature of maps created by a SLAM processbeing used to help a robot navigate to goals, such as themuseum robot Minerva. However most of these resultshave been generated from almost decoupled systems— one to generate a high resolution occupancy map,and one to process the occupancy map and generatea route to the goal. Apart from relying on a highfidelity map, these approaches also bear little plausibilitywith respect to what happens in nature. Animals arenot equipped with high resolution range sensors, andgenerally navigate using a varying mixture of visual

External Vision

SenseLocal View (LV)

Pose Cells (PC)

Path Integration

(PI)Internal Sensing

Goal Memory

(GM)

Experience Map

Goal

Navigation

Pose−View Map

Figure 2: RatSLAM structure

and idiothetic cues. More biologically plausible goalnavigation techniques have been developed and tested[Gaussier and Zrehen, 1994], but these are generallycreated without the focus of robust performance inlarge scale complex environments and do not achievecomparable results to the mathematical methods.

3 RatSLAM

The RatSLAM system is based on a system of neuralnetworks inspired by the rodent hippocampal complex.These neural networks learn a distributed representationof the environment. In this section we start by describingthe system architecture, before moving on to discussthe limitations that spurred the development of theexperience mapping technique.

3.1 Overall System

The architecture of the RatSLAM system is shown inFigure 2. The robot’s pose is represented by activity ina competitive attractor neural network called the posecells. Wheel encoder information is used to performpath integration by appropriately shifting the currentpose cell activity. Vision information is converted intoa local view representation that is associated with thecurrently active pose cells. The association between localview and pose is stored in weighted connections betweenthe respective neural networks. A familiar visual scenecauses activity to be injected into the particular posecells linked to the corresponding local view, enabling therobot to re-localise based on familiar visual input.

3.2 Pose Cells

The pose cells are implemented as a competitive attrac-tor network, a neural network that converges to a stablepattern of activity across its inputs. Each unit in thenetwork excites units close to itself and inhibits thosefurther away, which leads to a clump of activity knownas an activity packet eventually dominating. Activityinjected into the network near this winning packet willtend to move that packet towards the point of injection.Activity injected far away from the activity packet willcreate another packet that competes with the original.If enough activity is injected, the new packet can ‘win’

x

y

Pose Cells

(PC)

Local View

Cells (LV)

ViB(i)(xy )

Pxy

Figure 3: The pose cell and local view cell structures.Associative links are learnt between an activated localview cell Vi and an activated pose cell Pxyθ. Active localview cells inject energy into the pose cells through theselinks.

and the old packet will disappear. The pose cells arearranged in an (x, y, θ) pattern, allowing the robot tosimultaneously represent multiple pose estimates in x, yand θ (Figure 3). For indoor experiments each pose cellinitially represents approximately 0.25m×0.25m in areaand 10 degrees in bearing.

3.3 Path Integration

The path integration process updates the pose activitypacket(s) by injecting activity into the pose cell networkbased on robot wheel encoder velocities. The processneed not necessarily be Cartesian, although the imple-mentation described here is roughly Cartesian to aid invisualization and weight assignment. Initially movementof energy in the pose cell matrix is influenced entirelyby the path integration process, but as the robot learnsmore of the environment’s visual appearance, the localview system starts to become more dominant.

3.4 Local View

The local view module is a collection of neural unitsthat represent what the robot sees through the on-boardcamera in a form that can be associated with the posecell representation. Camera output is processed througha sum of absolute differences view matcher, that classifiesscenes as new or already seen. The local view structureconsists of a one-dimensional array of neural units, eachof which responds to a different visual scene. Explorationof large indoor environments typically recruits a fewthousand neural units responding to distinct views. Thedetails of the vision system architecture are described atlength in an earlier paper [Prasser et al., 2004].

The local view units are associated with the pose cellunits using Hebbian learning; weights between concur-rently active units are increased in strength. As theweights develop, they inject activity from the local viewunits into the corresponding pose cell units, so that therobot can recall its pose based on familiar visual input.There are no distinct learning and recall phases — theyboth occur continuously and concurrently.

3.5 Spatial Continuity

Our recent work has focused on extensive testing toevaluate the suitability of the RatSLAM system for longterm (on the order of days) experiments in larger, morecomplex environments. This testing has revealed thatthe original goal memory system, whilst robust to smallspatial discontinuities, relied on the RatSLAM environ-ment representation being approximately Cartesian. Astest duration and environment complexity increased, theworld representations became increasingly topologicaland less Cartesian in nature. Large topological re-localising ‘snaps’ in the world representation resulted infailure of the goal memory system.

As a robot explores an environment, path integrationand visual input drives pose cells to become associatedwith places and orientations in the environment. Pathintegration drives local movement of energy around thepose cell matrix. However visual input can cause globalre-localisation snaps between cells a long way apart inthe pose cell matrix, but which are associated with placesclose together in the real world. Figure 4(a) shows thetesting environment for a 2.5 hour experiment, with thepath the robot’s exploration algorithm used through theenvironment shown by the thick solid line. Travelling atan average speed of around 300 mm / second, 2.5 hourswas enough time for the robot to thoroughly exploreevery possible route through the environment.

Figure 4(b) shows the path that the dominant energypacket traced through the pose cell structure during thisexperiment. The figure is a 2D projection of the 3Dpose cell matrix — movement in the third dimension(θ) is not shown in this diagram. The trajectory isshown by the thicker solid lines, with re-localisationsnaps shown as thinner straight lines. At the right ofthe diagram a number of re-localising snaps can be seenwhere, driven by visual input, the peak energy locationin the pose cell network snapped from one location toanother. The physical reality represented by these snapshowever was a small amount of movement along themain corridor. There is a clear discrepancy between thedistance represented in the pose cells and the distance inthe physical real world. The same problem exists withorientation — there is a significant orientation differencein the pose cell network before and after the snaps, yet inthe physical world the robot had travelled in a straightline, with no orientation change.

The vast majority of conventional SLAM techniquessolve this problem by forcing a single representationof the environment. However there is evidence in thebiological literature that rats do not necessarily formone coherent representation of the world, especially forcomplex, visually ambiguous environments [Skaggs andMcNaughton, 1998]. Experiments have suggested thepossibility of rats switching between multiple represen-

Main Corridor

Open

Plan

Space

Laboratory

25 m

13 m

Offices

AB

C

(a) Robot’s actual path through theoffice environment.

y’

x’

D

E

F

(b) Path of maximum energy peakthrough the (x′

, y′) plane of the pose

cell matrix. Each grid square is 4 by4 pose cells.

Figure 4: Office floor environment.

tations of the same environment as they move around,although the basis for the switching is unclear.

3.6 Multiple Representations

One of the phenomena that can occur in RatSLAMinvolves more than one group of pose cells, which arefrom separate parts of the pose cell matrix, representingthe same physical location in the environment. This isapparent when looking at the representation of the maincorridor by the pose cells in Figure 4(b). The circledareas D and E show straight paths in different areasof the pose cells which represent travel along the samephysical corridor, labelled A in Figure 4(a). The largenumber of re-localising snaps shows frequent switchingbetween alternate representations of the corridor.

For most SLAM systems this would be an unaccept-able phenomenon and would cause catastrophic failurewhen the representation was used to perform goalnavigation. The initial goal memory implementationdescribed in [Milford et al., 2005] broke down in complexenvironments because it was based in the pose cellnetwork co-ordinate space and could not deal withlarge jumps around the pose cell matrix. Selecting agoal location was also difficult for a human user, forin order to specify a goal location the user had tomark all the representations of that goal location inthe pose cell matrix. Evidence that rodents can formmultiple representations of their environment [Skaggs

and McNaughton, 1998] suggested that the goal memoryimplementation rather than the pose cell world repre-sentation was at fault. Unfortunately, unlike researchon place and head direction cells [Taube et al., 1990;Arleo et al., 2001], there has been little light shed on theexact mechanism rats use to navigate to goals.

3.7 Hash Collisions

The reverse phenomenon to multiple representations canalso occur — multiple physical places can be associatedwith the same pose cells. At point F in Figure 4(b) thesame area of pose cells represents two distinct places inthe environment, labelled B and C in Figure 4(a). Thisphenomenon is similar to that of hash collisions whenusing a hash table. This means that when the pose cellsin that area of the matrix are active, there is ambiguityregarding the corresponding robot pose in the physicalworld.

4 Experience Mapping

The experience mapping technique was developed asa solution to these problems. It solves or avoidsthe spatial continuity, hash collision and multiple rep-resentation issues and produces human-friendly worldrepresentations; a human can easily associate parts ofthe map with places in the physical world. Experiencemapping is implemented in a module that builds ontop of the world representations in the pose cells andlocal view cells. This method allows the productionof spatially continuous representations, which in anylocal area closely represent the Cartesian arrangementof the environment. The method involves continuouscorrection of this experience map based on informationabout the local relative position and orientation ofconnected experiences.

4.1 Experience Generation

To produce a spatially continuous world representation,output from the pose cells and local view cells is usedto create a map made up of robot experiences. Arobot experience is a snapshot of the robot’s state ata point in time, containing information about the poseand visual scene associated with the experience, as wellas odometric information about transitions between itand other connected experiences.

Each experience’s relationship to the pose cell andlocal view networks can be described by 4 state variables:

(x′, y′, θ′, V ) (1)

where x’, y’, and θ′ are the three state variables thatdescribe the location of the cells within the pose cellmatrix associated with the experience, and V describesthe visual scene associated with the experience. Figure5 shows the conceptual representation of an experience.

Each experience has a zone of influence. This zoneof influence describes the value range within each of the4 state variables when this experience is activated. Inthe (x’, y’) pose cell plane this zone describes a circulararea — the experience has the highest energy level whenthe maximally activated pose cell is at the centre of thisarea. This relationship is described by Equations 2, 3,and 4:

r =√

(x′

pc − x′

i)2 + (y′

pc − y′

i)2 (2)

rratio =r

rinfluence

(3)

Ex′y′ = max(1.0 − rratio, 0) (4)

where r is the distance in the (x′, y′) plane of the posecells from the point of maximal energy to the positionassociated with the experience, (x′

pc, y′

pc) are the co-ordinates in the pose cell (x’, y’) plane of the point ofmaximal energy, (x′

i, y′

i) are the co-ordinates in the (x’,y’) plane of the cells associated with the experience, andExy is the energy component contribution.

In the θ′ dimension, the zone of influence describesa range of orientations within the pose cells that theexperience is associated with. This zone is centeredaround the experience’s preferred orientation — it ismost energized when the maximally activated posecell’s orientation matches this preferred orientation. Asthe peak pose cell rotates away from the experience’spreferred orientation, the energy level of the experiencewill decrease. Equations 5, 6, and 7 describe the angularzone of influence:

∆θ′ = |θ′pc − θ′i| (5)

θ′ratio =∆θ′

θinfluence

(6)

Eθ′ = max(1.0 − θ′ratio, 0) (7)

where ∆θ′ is the minimum angular difference betweenthe most likely pose cell orientation θ′pc and the pose cellorientation θ′i associated with the experience, and Eθ′ isthe angular energy contribution.

For the V variable which describes the visual scene,the zone of influence is discrete — each experience’s zoneincludes exactly one visual scene. The visual scene actslike a switch for the experience, switching it on or off.

EV =

{

0 if V 6= Vi;1 if V = Vi;

(8)

where V is the current visual scene, Vi is the visualscene associate with experience i, and EV is the visualscene energy component.

θ

(x, y)rinfluence

θinfluence

V

Figure 5: Basic experience

Together Equations 4, 7, and 8 give the total energylevel of an experience, Ei:

Ei = EV × (Exy + Eθ) (9)

As the robot moves around a novel environment, itneeds to generate experiences to form a representationof the world. Learning of new experiences is triggerednot only by exploring in new areas of an environment,but also by visual changes in areas the robot has alreadyexplored. In addition, if there are multiple paths throughthe pose cells that represent traversing the same corridor,the system will learn multiple sets of experiences, eachset associated with one of the paths through the posecells. This by itself would lead to the goal memoryproblems dealing with multiple representations, howeverthis problem is avoided through map correction, whichis discussed in Section 4.3.

RatSLAM continuously checks whether given thecurrent visual scene and pose cell activity, it is in apreviously experienced place. If none of the current ex-periences have a positive energy level, a new experienceis learnt, as shown in Equations 10 and 11.

Emax = max(E) (10)

where E is the vector of experience energy levels, andEmax is the maximum experience energy level.

Ne =

{

Ne if Emax > 0;Ne + 1 if Emax 6 0;

(11)

where Ne is the number of experiences.

4.2 Experience Transitions

As the robot moves around the environment, the systemalso learns experience transitions. These transitionsrepresent the physical movement of the robot in theworld as it moves from one experience to another. Atransition from one experience to another is representedby three variables θij , φij , and dij , as shown in Figure 6.

Two angular variables are required to fully describean experience transition. θij describes the difference

Expected pose

of experience j

Actual pose of

experience j

(xi, yi)

(xj , yj)

θi

θj

θij

φijdij

Figure 6: An experience transition. Shaded circles andbold arrows show the actual pose of the experiences.The dotted circle and arrow shows the expected poseof experience j based on the transition information.

between the absolute orientation of the first experienceθi and the angle to the location of the second experience(xj , yj) within the experience map co-ordinate space.φij however describes the angular difference betweenthe orientation of the two experiences, without anyregards to their relative (x, y) location. dij describesthe translational distance between experiences i and j.

When there is a large re-localising snap in the pose cellmatrix, the experience transition preserves informationabout the physical movement of the robot throughthat snap. This information is used to perform mapcorrection that helps generate a spatially continuousmap, as described further in Section 4.3.

4.3 On-line Map Correction

By introducing the visual scene information as a fourthstate variable of the experiences we avoid the hashcollision problem described in Section 3.7. However,there is still the problem of multiple representations ofthe same physical location in the environment. Thisbecomes quite apparent when closing the loop — anySLAM system that takes into account ambiguity in theenvironment cannot instantaneously re-localise at themoment it starts on the second loop of an environment.Rather, it takes some time before enough sensory inputis received to confirm that it is back at the start ofthe second loop. Conventional SLAM systems usingEM techniques then do a global map correction thateffectively coalesces the multiple representations of thestart of the loop into a single one.

Rather than attempting to combine multiple repre-sentations, the experience mapping technique overlapsthem within the experience map’s own (x, y, θ) co-ordinate space through a process of map correction(not to be confused with the (x’, y’, θ′) co-ordinatesthat describe the locations of the pose cells associatedwith the experience). This preserves the multiple rep-resentations and the information they possess about the

environment but creates a human-friendly map wherethese representations of the same physical place overlap.

The first experience’s pose within the experience mapco-ordinate space is initialised to an arbitrary locationand orientation. The pose of each subsequent experiencewithin this experience map co-ordinate space is initial-ized using the pose of the last activated experience andthe relative odometric information about the transition.When re-localising after significant time spent in a novelpart of the environment, the robot will snap from thenew experience it has most recently learned to an old, fa-miliar experience. This will result in a large discrepancybetween the transition’s relative odometric informationand the difference between the two experiences (x, y, θ)co-ordinates.

The map can be made to be spatially continuous byminimizing these discrepancies. This can be achieved byconsolidating the expected pose of each experience basedon relative odometric information and their current pose.Equations 12, 13, and 14 describe the update of thephysical pose state of each experience.

∆θi = αθ

[

Nfrom∑

j=1

(

θj−θi−φij

)

+

Nto∑

k=1

(

θk+φki−θi

)

]

(12)

where ∆θi is the overall change in orientation ofexperience i, αθ is the angular learning rate, Nfrom is thenumber of links from experience i to other experiences,θj is the angle of the linked to experience j, θi is thecurrent absolute angular orientation of experience i, φij

is the expected angular rotation from experience i toexperience j, and Nto is the number of links from otherexperiences to experience i.

∆xi = αd

[

Nfrom∑

j=1

(

xj − dij cos(θi + θij) − xi

)

+

Nto∑

k=1

(

xk + dki cos(θk + θki) − xi

)

]

(13)

where ∆xi is the overall change in x co-ordinate ofexperience i, αd is the translational learning rate, xj

is the x co-ordinate value of the linked to experiencej, dij is the odometric translational distance betweenexperience i and j, θij is the relative angular orientationfrom experience i to j, and xi is the current x co-ordinatevalue of experience i.

Table 1: Experience map learning rates

Learning Rate

Variable

Value Description

αθ 0.5 Orientation update

αd 0.5 Translational update

∆yi = αd

[

Nfrom∑

j=1

(

yj − dij sin(θi + θij) − yi

)

+

+

Nto∑

k=1

(

yk + dki sin(θk + θki) − yk

)

]

(14)

where the naming convention is as for Equation 13,with sin instead of cos.

4.4 Map Learning Rates andComputability

The experience map is subject to the same constraintsof any network style learning system — appropriatelearning rates must be used to balance rapid convergencewith instability. The nature of the network is such thatthe vast majority of inter-experience links have a highdegree of consistency between their relative odometricinformation and their (x, y, θ) state, with only significantdiscrepancies whenever there is a re-localising snap. Thisallows a high learning rate to be used when comparedwith a self organizing network that has all its valuesrandomly initialized. Table 1 shows the learning ratesused to update each experience’s pose state. Largerlearning rates were found to result in network instability.

Experience map computation scales directly with thenumber of experiences in the map. To achieve ap-propriate coverage of an environment the number ofexperiences must be approximately proportional to thearea of the environment. The bulk of the computationalresources is shared by the experience mapping techniqueand the pose cell matrix, which computationally alsoscales directly with the area of the environment. Ona 1.1 GHz laptop RatSLAM mapped at real time speeda 43 metre by 13 metre building floor.

5 Simulation Experiments

Simulation allows us to perform a large number ofexperiments quickly in complex environments which arenot necessarily feasible to set up in reality. In Section 6we describe a real world experiment which validates theresults obtained in simulation.

5.1 Simulation Description

We used a simulator developed in our laboratory thatcan load simple object maps of environments designed

Start

7m

7m

Figure 7: Small loop environment

y’

x’

(a) Pose cell map

x, (m)

y, (m

)

(b) Experience map

Figure 8: Loop closing under a 1.5 deg/sec odometricbias. The grid squares are 250mm by 250mm.

in a world editor. Lengths and orientations of walls arespecified. Noisy, ambiguous visual information is createdusing an algorithm that creates visual scene numbersbased on the simulated robot’s pose. False positivematches can occur — at random times the simulatedrobot will falsely recognise a familiar scene when in acompletely novel part of the environment. Simulationswere performed on a 1.1 GHz Pentium III laptop atreal time speed. Extensive experimentation in the pastboth in simulation and the real world has confirmed thatthe performance in simulation is indicative of real worldresults.

5.2 Closing Simple Loops UnderOdometric Error

RatSLAM can consistently close loops of varying com-plexity and length in a range of indoor and outdoorenvironments. This loop closing is achieved throughre-localisation snaps in the pose cell matrix, driven byvisual input from familiar scenes. These snaps involvethe peak energy location in the pose cell matrix snappingfrom one location to another. This set of simulationsinvolved 15 minute tests in a square corridor loopenvironment as shown in Figure 7. The robot’s startinglocation and orientation are shown by a small circle andarrow at the bottom left of the figure.

Since the total loop distance was quite short (about 18

y’

x’

(a) Pose cell map

x, (m)y, (m

)(b) Experience map

Figure 9: Loop closing under a 3.0 deg/sec odometricbias.

y’

x’

(a) Pose cell map

x, (m)

y, (m

)

(b) Experience map

Figure 10: Loop closing under a 6.0 deg/sec odometricbias.

metres), there was only a small amount of accumulatedodometric error after one loop was completed. In orderto demonstrate the spatial continuity of the experiencemap, a range of odometric biases were introduced. Thesebiases allowed us to show more clearly the differencebetween the pose cell and experience map world rep-resentations.

Figure 8(a) shows the trajectory of the energy peakthrough the (x′, y′) co-ordinate space of the pose cellmatrix, when a constant odometric bias of 1.5 degreesper second was introduced. The effect of the bias isclear — since movement of energy around the pose cellmatrix depends on the path integration module, the 90degree corners in the real world environment are realisedas significantly less than 90 degree turns in the pose cellmatrix. Straight paths are also represented as slightlycurved routes in the pose cells. There is a region of re-localisation snaps at the beginning of the loop, wherevisual input has caused the location of peak activityto snap from one location to another. These snaps arerepresented by the thin straight lines.

In contrast the experience mapping technique hasproduced a continuous, spatially representative map, asshown in Figure 8(b). The separation of the experiencesbefore and after the re-localisation snap is indicativeof the physical movement of the robot rather than themovement of the peak energy packet through the posecell matrix.

Start

13

m

43m

(a) Path of robot through simulatedworld. Starting robot pose shown bycircle and arrow.

y’

x’

G H

(b) Pose cell map. Each grid squareis 4 by 4 pose cells.

(c) Experience map. Each grid squarerepresents 1 square metre.

Figure 11: Full floor environment.

Figures 9 and 10 show the pose cell and experiencemap world representations for odometric biases of 3deg/sec and 6 deg/sec. The increased path integrationerror is apparent in the pose cell matrix representations,especially in Figure 10 where the trajectory throughthe pose cells is almost a straight line. However, theexperience maps for all three cases are consistent andrepresentative of the actual physical environment. Theonly significant variation that occurs is in their overallorientation. This is a result of no experiences beingglobally anchored — all experiences are free to moveand rotate based on the network dynamics, so the finalstable map can vary in its position and orientation, butis always internally consistent.

5.3 Full Floor Office Environment

As the environment becomes larger and more complex,the discontinuity of the pose cell trajectories and thefrequency of re-localising snaps increases. Figure 11(a)shows the robot’s path through a simulated environmentbased on the complete floor of our office building. Thisenvironment contains loops of varying sizes and shapes,as well as long corridor sections and open areas.

The trajectory of the energy peak through the (x′, y′)co-ordinate space of the pose cells is shown in Figure

Figure 12: Photo of open plan office environment.

11(b). There is a significant amount of re-localisationpresent as well as multiple representations of places inthe environment, such as the two loops at G and Hthat represent laps of the same room. The experiencemap (Figure 11(c)) however closely matches the actualenvironment. All the multiple representations have beengrouped into overlapping areas.

6 Real Robot Experiments

To validate the simulation results we tested the experi-ence mapping technique on a Pioneer 2DXE mobile robotequipped with a 50 degree field of view forward facingcamera, a scanning laser and wheel encoders. Somevisual processing was performed on the robot’s on-board400 MHz Athlon K6 processor, and the informationwirelessly transmitted to a 1.1 GHz Pentium III laptopwhere the rest of the RatSLAM system resides, includingthe experience mapping component. System iterationand information updating were performed at an averagespeed of 7 Hz.

The testing environment for the experiment was an80m2 area of open plan office space in our building,as shown in Figures 12 and 13(a). The environmentconsisted of a number of desk cloisters, chairs, fridges,office doors and other objects. The robot was setloose for 15 minutes in this environment using its ownexploration algorithm, which attempts to learn all thepossible routes through the environment.

Figure 13(b) shows the pose cell trajectory for theexperiment. There are many re-localising snaps andmultiple representations present. Figure 13(c) showsthe corresponding experience map for this environment.The re-localising snaps are gone, and the multiple rep-resentations have been grouped into overlapping areasin the map. The map is human-friendly — a person caneasily connect places in the map to places in the physicalenvironment.

7 Discussion

Local areas of the experience maps preserve the Carte-sian properties of the area of the environment theyrepresent. This correlation is preserved through min-imisation of the discrepancies between the relative posesof the experiences and the odometric information abouttransitions between the experiences. This means that

10 m

8 m

Start

(a) Path of robot through open planoffice space.

y’

x’(b) Pose cell map. Each grid squareis 4 by 4 pose cells.

(c) Experience map. Each grid squarerepresents 1 square metre.

Figure 13: Open plan environment

there is no guarantee that on a global scale the mapwill be completely Cartesian — for instance straightcorridors may be slightly curved in the experience map.In certain situations, for example when the robot isin a very long narrow corridor, the experience mapof the forward and backward paths may not overlap.As external observers we know that it is the samecorridor going either way, but from the perspective of arobot with a limited field of view and appearance basedSLAM technique, it has no information linking the twodirections apart from odometric information. Since a

long time may pass between traversing the corridor oneway and returning back the other way, the experiencemap representations of both directions may not overlapexactly.

One obvious solution to this problem is for the ex-ploration algorithm to encourage ‘turn arounds’ in longnarrow environments. By turning around and travellingback the other way, a set of odometric transition linksare learnt between the experiences representing either di-rection of travel along the corridor. The map correctionalgorithm then overlaps the two representations since itnow has odometric information linking them. We arecurrently working on behavioural based solutions to thisproblem.

The visual component of an experience is dependenton the lighting conditions of the environment. Theindoor environments tested in have so far had relativelyconstant illumination. However, in experiments on anoutdoor robot tractor we have tested a histogram basedvision system which relies on only the hue and saturationcomponents of HSV colour space, thereby introducingsome robustness to light intensity variations over time[Prasser et al., 2005].

Future work will investigate a goal memory systembased on the experience map. Spatially continuousmaps are ideal for goal directed navigation, because inany local area the map closely represents the geometricarrangement of the environment. Since the map groupstogether multiple representations, goals can be speci-fied by a single mouse click rather than by manuallypicking all the alternate representations in the pose cellmatrix. By incorporating time durations into the inter-experience link transitions, the quickest route to anylocation in the map can be found. The experience mapprovides both the shortest continuous global route to agoal as well as locally accurate spatial representations,which can be used to make decisions at multiple-choicelocations in the environment.

8 Conclusion

This paper has described the methodology and integra-tion of a new technique known as experience mappinginto the RatSLAM system. Results were presentedfor a range of simulated environments and a mediumscale real world environment. These results demonstratethat the experience mapping technique can process theworld representations generated by RatSLAM to createon-line a map that is spatially continuous and thatgroups multiple representations into overlapping areas.The continuity and local Cartesian properties of theexperience maps ensure they are human-friendly and aresuitable for goal directed navigation, regardless of thetopological nature and discontinuity of the underlyingRatSLAM world representations.

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