Map Matching for SLAM with Multiple Robots in Different Moments

Andr

e Felipe da Silva Oliveira

, Diego de Oliveira Dantas

, Doriedson Mendonc¸a Corr

Areolino de Almeida Neto

and Will Ribamar Mendes Almeida

Science and Technology Center, State University of Maranh

ao (UEMA), Av. Lourenc¸o Vieira da Silva, S

ao Lu

ıs, Brazil

Department of Computer Engineering, CEUMA University (UNICEUMA), S

ao Lu

ıs, Brazil

Department of Informatics, Federal University of Maranh

ao (UFMA), S

ao Lu

ıs, Brazil

will.almeida@ceuma.br

Keywords:

Mapping, Robot Navigation, Map Matching, Map Merging, Map Fitting, SLAM.

Abstract:

This paper proposes a method for matching of occupancy grid maps in image form, in which a new way

of searching for similarities is developed. The maps used were obtained via SLAM. This work uses image

processing techniques to extract map features and create an alphabet, by means of features relationships, for

each map. Candidates for matching points are found from the comparison between members of the alphabets

generated. After the comparison, the possible matchings are veriﬁed from candidate points, applying a metric

of similarity. Thus, matching points that provide greater similarity are chosen as the best current points. These

operations are repeated each time that a new updated map is provided. Thus, the similarity rates of the best

points of the previous iteration are updated, and the new best matching points are calculated for the current

iteration; in this way, the matching points that have the highest similarity ratio among the previous iteration

and the current iteration are chosen as the best current points. The results obtained are promising, since in most

tests performed, it was successful in ﬁnding the correct matching between the maps. A quantitative analysis

was also performed on success cases, which demonstrated the efﬁciency of the method and the proximity of

the map matching method with single robot mapping methods.

1 INTRODUCTION

For a mobile autonomous robot to perform a task, it

is often necessary to have knowledge about the envi-

ronment in which it is inserted, because through this

knowledge the robot can make decisions and move

more efﬁciently. However, a map of the environment

is not always available, making it necessary to have a

mapping process in the place, whether performed by

the robot itself or not.

The mapping processes can be performed in vari-

ous ways. A very common example in Robotics is the

use of SLAM (Simultaneous Localization and Map-

ping) techniques, in which the robot maps the en-

vironment at the same time it is located. Mapping

processes performed by different robots have been in-

creasingly used in order to improve mapping speed

and make the process more reliable. In this way, if

one of the robots becomes unavailable, the others con-

tinue the process.

Many map matching works (Bresson et al., 2013),

(Thrun and Liu, 2005), (Cunningham et al., 2012),

(Koch et al., 2016), (u. N. Jafri and Chellali,

2013),(Jian et al., 2017), (Blanco et al., 2013), (Birk

and Carpin, 2006) use robot-generated maps via

SLAM. Thus, some of these works (Bresson et al.,

2013), (Thrun and Liu, 2005), (Cunningham et al.,

2012), (Jian et al., 2017), (Blanco et al., 2013) tend

to focus their efforts on extracting features directly

from the mapping process, relating matching directly

to the mapping process itself. However, in this way,

the matching process becomes highly dependent on

the type of SLAM used and the speciﬁcs characteris-

tics extracted of this SLAM.

There are several mapping techniques from

SLAM with multiple robots. These techniques usu-

ally use extracted features during the individual

SLAM of each robot of the team, so that through these

features it is possible to ﬁnd several types of associa-

tions.

This work proposes a method of map matching,

which is used to compare geometric features extracted

from the corners detected in the maps. This method

was developed in order to work with occupancy grid

210

Oliveira, A., Dantas, D., Corrêa, D., Neto, A. and Almeida, W.

Map Matching for SLAM with Multiple Robots in Different Moments.

DOI: 10.5220/0007395902100217

In Proceedings of the 11th International Conference on Agents and Artiﬁcial Intelligence (ICAART 2019), pages 210-217

ISBN: 978-989-758-350-6

type maps as image so that it does not rely on internal

variables SLAM method used for mapping, making it

therefore a more usefull method and easy portability.

This paper is divided as follows: in Session 2 it

is presented how the map matching was made, dis-

cussing the preprocessing applied in the maps, the

process of extraction of features (corners), the cre-

ation of alphabets through the relationship geometric

of these features, the comparison of these alphabets,

the selection of the best feature to ﬁnally perform the

matching of the maps; in Session 3 it is discussed

the methodology used in the preparation of this work,

considering the used machine conﬁgurations and the

robot model used in SLAM process; in Session 4 it

is discussed on the results obtained, in both qualita-

tive and quantitative analyzes and in Session 5 it is

presented the conclusion as well as future works.

2 MAP MATCHING THROUGH

FEATURES RELATIONSHIP

This work sought to make the matching of maps per-

formed by several robots. The matching between the

provided mappings is done for each two maps, gener-

ating a ﬁnal map at the end of the process.

The maps provided for the method are occupancy

grid type (Elfes, 1989), which is characterized by an

occupation matrix and, when treated in image form,

represented in gray scale, where the black color ex-

presses obstacles detected in the environment, the

white color expresses regions mapped without obsta-

cles, and the gray color represents unmapped regions.

The image format used in the method was Portable

Graymap (pgm), because it is a portable format and

has small dimensions, while also having a good scale

conversion ratio to the original environment being

mapped.

2.1 Map Matching

In order to match the individual maps obtained by a

robot team and thus generate a ﬁnal map, it is neces-

sary to follow a sequence of steps.

Because the proposed method is based on map

matching through the mapping process, it works so

that the system receives maps of each new state that

is provided or saved during the process. In the Algo-

rithm 1, it can be observed the procedure, in which the

system receives the states of each mapping and sup-

plies them to the map matching function. This func-

tion returns the resulting matching and matching rate,

which deﬁne how similar the map states are (relative

to the selected matching points). After this step, it is

checked if the matching rate is higher than the best

matching rate thus far; if the matching rate is higher,

then the best matching rate is updated along with the

best matching 1. In this work, map 1 is represented

by δ

, while map 2, by δ

, and mapping process 1 is

represented by ∆

, while mapping process 2, by ∆

The best current matching rate is represented by Ψ;

while the current matching rate, by ψ. The best cur-

rent matching is represented by Λ, while the current

matching between the maps is represented by λ.

Algorithm 1: Map Matching of the mappings pro-

cess.

1 Function MapMatchingMapping (∆

, ∆

)

2 begin

3 ψ = 0

4 Ψ = 0

5 δ

= empty

6 δ

= empty

7 Λ = empty

8 while (exist maps in ∆

or ∆

) do

9 if (exist maps in ∆

) then

10 δ

= next map from ∆

11 end

12 if (exist maps in ∆

) then

13 δ

= next map from ∆

14 end

15 λ, ψ = MapMatching(δ

, δ

) if (ψ >

Ψ) then

16 Λ = λ

17 Ψ = ψ

18 end

19 return Λ

20 end

21 end

The matching process can be observed in the func-

tion exposed in the Algorithm 2, in which the proce-

dure receives two maps as input. Thus, a sequence

of procedures for obtaining matching is performed.

Initially, the maps are preprocessed, so that the most

obvious noises are smoothed and the main features

of the maps are highlighted. After the preprocess-

ing, the features of the maps are extracted and then,

from geometric relationships among them, alphabets

are generated. By comparing the similarities of al-

phabet members, a set of points is considered to be

a candidate for matching points among maps (map-

ping points). From these matching points, the best

points are deﬁned based on a metric of similarity and

then the matching generated by points with greater

similarity are returned with associated similarity rate.

The map delta

converted to gray scale is represented

by α

, while the map delta

by α

. The features ex-

tracted from the map α

are represented by β

, while

In this work, alphabet is considered a set of symbols

with features that differentiate them from each other.

Map Matching for SLAM with Multiple Robots in Different Moments

211

the map α

, by β

. The matrix that stores the com-

pared alphabets is represented by γ. The current can-

didate points are expressed by φ, while the best cur-

rent candidate points are represented by Φ. The high-

est evaluation of the Jaccard similarity metric is rep-

resented by Σ.

Algorithm 2: Map Matching.

1 Function MapMatching (δ

, δ

)

2 begin

3 α

= PreProcess(δ

)

4 α

= PreProcess(δ

)

5 β

= ExtractFeatures(α

)

6 β

= ExtractFeatures(α

)

7 L

i,1

= GenerateAlphabet(β

)

8 L

z,2

= GenerateAlphabet(β

)

9 γ = CompareAlphabet(L

i,1

, L

z,2

)

10 φ = CandidateMatchingPoints (γ)

11 Φ, Σ = BestMatchingPoints (φ, α

, α

)

12 λ = Matching(Φ, δ

, δ

)

13 return λ, Σ

14 end

2.1.1 Preprocessing the Maps

Preprocessing of maps is a fundamental step, as it

smoothes the noise and highlights the features of the

maps.

The map is converted to grayscale, and then bina-

rized (Lopes, 2012), so that only whites and blacks

remain on the map. A example can be observed in

Figure 1 (a) represents the map to the system, and

Figure 1 (b) expresses the map after being binarized.

(a) Map provided. (b) Binary map.

Figure 1: Example of binarization of a map.

In order to close the discontinuities of walls that

are common in occupancy grids maps type and thus to

avoid wrong detections of corners , a sequence of two

erosions (das Grac¸as Medeiros et al., 2002) is applied

to ”thicken” the collisions of the map and then join

the points belonging to the wall, in order to promote

the continuities thereof. The result of the application

of two erosions in Figure 1 (b) can be observed in

Figure 2 (a), where it is veriﬁed that erosions promote

the closure of spacing on the walls of the map.

After closing the undue spacing on the walls of the

map, it is necessary to return the walls to their orig-

(a) After two ero-

sions.

(b) After two dila-

tions.

Figure 2: Example of erosion and dilation applied to a bi-

nary map.

inal thicknesses, so a sequence of two dilations (das

Grac¸as Medeiros et al., 2002) is applied to ”sharpen”

the walls of the map. The result of the dilations ap-

plied in Figure 2 (a) can be observed in Figure 2 (b)

and, comparing the result of Figure 2 (b) with the re-

sult of Figure 1 (b), it can be seen that a large part of

these discontinuities have been corrected.

In order to remove features originated from non-

relevant objects such as chair and table legs, a se-

quence of erosion, median blur (Gonzalez and Woods,

2010) and dilation is performed. The result of these

operations applied to the image in Figure 2 (b) can be

observed in Figure 3.

(a) Erosion. (b) Median

Blur.

Figure 3: Example of a sequence of erosion, median blur

and dilation.

2.1.2 Features Extraction

The SLAM maps are poor in features, so we chose

one of the most obvious features, which are the cor-

ners. As a corner detector, the Good features to track

(Shi and Tomasi, 1994), which is an extension of the

Harris corner (Harris and Stephens, 1988) method,

has a strong invariance of rotation and noise. Good

features to track were used because it selects corners

in a distributed form throughout the image, limiting

the maximum number of corners to be detected and

these reduce the complexity in the process of genera-

tion and comparison of alphabets.

The Good features to track technique is applied

right after the preprocessing of the mappings. In the

ﬁgure 4 it is possible to observe the corners that were

detected in the map of the Figure 3 (c).

With the detection of these corners, a list L

generated for each map of each robot with N

corners

detected in each map, where m represents the map, N

ICAART 2019 - 11th International Conference on Agents and Artiﬁcial Intelligence

212

Figure 4: Detection of corners using the technique Good

features to track.

the number of corners and L represents the list as seen

in the expression 1.

= (c

1,m

, c

2,m

, c

3,m

, ..., c

) (1)

2.1.3 Generation of the Alphabet

The alphabet is treated as a set of symbols with its

own characteristics. Each symbol is deﬁned by a par-

ticular corner and the characteristics of the symbol are

determined by the set of geometric relationships with

others belonging to the same map.

In order to relate a corner c

i,m

to another c

j,m

it has been deﬁned that the euclidean distance d

among them is subject to the following condition:

∀d

i, j

| D

max

≥ d

i, j

∧ D

min

≤ d

i, j

∧ D

max

≥

min

∧ i 6= j. In addition, that D

max

and D

min

are the

parameters that deﬁne the maximum and minimum

distances, respectively, of relationship among the cor-

ners.

To relate the corners, for each corner c

i,m

within

the list of corners L

, the distance from that corner

is calculated for all other c

j,m

of the same list, where

j 6= i. The corners within the range deﬁned by D

max

and D

min

are added to a list L

i,m

which represents the

alphabet and is related to the corner c

i,m

, as seen in

the Expression 2, where the value T

i,m

represents the

size of the list L

i,m

, that is, the number of other cor-

ners with which c

i,m

relates.

i,m

= (c

1,m

, c

2,m

, c

3,m

, ..., c

i,m

) (2)

After relationship among the corners, it is required

to calculate the second characteristic of each mem-

ber of the alphabet. The angle formed by the vec-

tors deﬁned by the corners c

i,m

is calculated for all the

members c

j,m

of its list L

i,m

. The calculation of all

the possible combinations within each list is achieved

and thus the alphabet is obtained that will be used for

comparison between the maps.

Each c

i,m

along its angles and calculated distances

relative to all other corners within a range of distances

delimited by D

max

and D

min

are a letter of the alpha-

bet. The purpose of delimiting these distances is to

prevent errors accumulated with the distance among

the corners interfering in the matching process.

Based on the Figure 5, one can observe visually

how the form of two alphabet members generated by

using the corners detected in Figure 4 looks.

(a) Member 1. (b) Member 2.

Figure 5: Members of an alphabet generated.

2.1.4 Comparison of Alphabet Members

In this step, a comparison is made among the mem-

bers of the alphabets, in which a comparison is made

based on the characteristics of distances and angles

related to each member.

Firstly, we compute each distance d

i, j

and d

z,k

re-

lated to each alphabet L

i,1

and L

z,2

, if these distances

are similar (or within a margin of error ε), we look

for other similar distances d

i,y

and d

z,q

, and if similar

distances, we seek to relate the angles. For the re-

lationship among the angles, the angle formed among

the distances in each map, angle a

i, j,y

for the distances

i, j

and d

i,y

and the angle a

z,k,q

for the distances d

z,k

and d

z,q

, if the angles a

i, j,y

and a

z,k,q

are similar (or

within a margin of error ν), then the matrix γ[i][z] s

incremented in the positions for each member of the

alphabet i and z.

2.1.5 Selection of Candidates for Matching

Points

After calculating the similarity among the corners of

each map, one must select the candidates for matching

points among the maps. This selection is performed

by searching for corners with greater similarities in

the matrix γ.

A maximum number candidates

number of can-

didates for matching points φ is established. For each

candidate φ

a,1

, a position of greater value is associ-

ated within γ, so that positions do not recur among

candidates. After deﬁning the ﬁrst matching point for

each candidate, the second point is deﬁned, where φ

a,2

receives the position with the highest value within γ

and the associated distances are similar (d

i, j

≈ d

z,k

are similar, i and z are related to φ

a,1

and j and k are

related to φ

a,2

). Thus, the candidates for matching

points to be tested in the next stage are selected.

Map Matching for SLAM with Multiple Robots in Different Moments

213

2.1.6 The Best Matching Points

After the candidate selection phase for matching

points, it is necessary to choose among them, that is,

the ones that ﬁt the best. This choice is made through

attempts to ﬁt among the maps, using the candidate

points as ﬁtting points.

In this way, the map δ

is translated and rotated

on the map δ

, so that the points (corners) c

i,1

and

z,2

referring to φ

a,1

, and also the points c

j,1

and c

k,2

referring to φ

a,2

, are respectively aligned.

After ﬁtting candidate points, it is possible to mea-

sure which of the ﬁttings has the highest degree of

similarity and, therefore, greater reliability. For this,

the Jaccard similarity metric σ (Jaccard, 1912), (Mil-

sztajn and de Geus, 2015) is applied between the

maps δ

and δ

, exempliﬁed by Figure 6 (a) and (b).

This metric requires that two operations should be

performed among the maps, the ﬁrst one is an inter-

section inter, exempliﬁed by Figure 6 (c), and the sec-

ond one is a union uni, exempliﬁed by Figure 6 (d).

In the result of these operations, the number of black

pixels at the intersection inter and the union uni is

counted, and then the amount of black pixels of inter

is divided by uni. In this way, we obtain the simi-

larity metric for each pair of candidates for matching

points; the pair that has greater similarity is chosen as

the current best matching points.

(a) Map 1. (b) Map 2.

Figure 6: Example of union and intersection application on

two maps.

2.1.7 Matching among the Maps

As maps 1 and 2 have already been aligned in the pro-

cess of choosing the best matching points , a new im-

age is generated that will receive the characteristics of

both maps δ

and δ

. This transfer of characteristics

follows the following rules:

• Gray color is the ﬁrst one to be transferred to im-

age;

• White color is the second one to be transferred to

image and overlaps with gray color;

• Black color is the third one to be transferred to

image and it overlaps with gray and white colors.

So, we have the new ”matching” map λ gener-

ated. An exempliﬁcation of the result of the matching

can be observed in Figure 7 (c), where one can ob-

serve the map obtained from a matching of maps in

Figure 7 (a) and (b).

(a) Global map ref-

erence 1.

(b) Global map ref-

erence 2.

ing.

Figure 7: Global reference in the matching among two

maps.

The global reference of the map δ

remains the

same as the mapping process. The global reference

of the map δ

receives the same translation and ro-

tation operations as δ

received. In Figure 7 (a), the

global map reference is referenced by the blue arrow,

where the direction of the arrow represents the ini-

tial orientation of robot 1 and the base of the arrow is

the initial position of the robot 1 that mapped δ

. In

Figure 7 (b), the global reference of the map is ref-

erenced by the red arrow, where the direction of the

arrow represents the initial orientation of robot 2 and

the base of the arrow is the initial position of the robot

2 that mapped the δ

. Thus, when the translation and

rotation operations are applied to δ

, these same oper-

ations are also applied in the global reference of robot

2, so that the result in the matching can be observed

in Figure 7 (c), where the global reference of robot 2

is adjusted to match the maps.

ICAART 2019 - 11th International Conference on Agents and Artiﬁcial Intelligence

214

3 METHODOLOGY

3.1 Materials

For the development of this work was used a com-

puter with the following machine conﬁguration: Kaby

Lake processor I7-7700HQ (6 MB cache up to 3.8

GHZ), a 32 GB DDR4 RAM (2133 MHZ) mem-

ory, a NVIDIA GEFORCE GTX 1050 GPU (4 GB

GDDR5) video card, a 1 TB SSHD with 8 GB SSD,

a 480 GB (500 MB / S) SATAE M.2 SSD. This was

used with the Ubuntu 16.04 LTS 64-bit operating sys-

tem.

As a robot manipulation system, the Robot Oper-

ational System (ROS) (ROS, 201) KINETIC version

was used. As a robotic simulator, the V-REP was

used. As generator of random environments, the V-

REP map generator

(Jelinek, 2016) was used. As a

system for mapping, the SLAM Gmapping (Grisetti

et al., 2007) Toolkit was used. The Oracle VM Vir-

tualBox software was also used for operating system

virtualization.

3.2 Robotic Model

For simulation in the V-REP, the model of robot

mecabot (Dantas, 2017)

was used.

For this work, the model used did not contain the

robotic arm and the vision sensor it was replaced by a

fastHokuyo laser scanner available on the V-REP. In

this sensor it was introduced, via textit script, an er-

ror of 0.01 meters. It were also introduced a reading

aperture of 240 degrees around the robot and a read-

ing radius of 5 meters.

3.3 Methods

In order to carry out this work, it was necessary to

use the materials described in the materials topic 3.1.

So the software described was installed on an Ubuntu

virtual machine, with the conﬁguration of four pro-

cessors and 16 GB of RAM.

Using the V-REP map generator connected with

the V-REP, it was possible to generate and save scenes

from ten random and different environments in-door.

These environments were generated with the purpose

of being mapped in order to generate a database for

the tests of map matching.

With the V-REP integrated to the ROS, for each

environment map generated previously, the mapping

Available in: https://github.com/Lukx19/Vrep-map-

generator

Robot model details available in:

https://tede2.ufma.br/jspui/handle/tede/2062

of these environments was performed through gmap-

ping, the control of the robot was performed manu-

ally, via keyboard, through the teleop twist keyboard

application

Thus, the teleop twist keyboard application was

performed applying a linear velocity of 0.8 m/s and

an angular velocity of 1.61 rad/s via human con-

trol; ﬁve maps were also performed for each gener-

ated environments (for each environment, each map-

ping occurred with different poses). Thus formed a

database of 50 mappings. In order to attempt a match-

ing among maps, we have always explored an area in

common among the mappings of the same environ-

ment. For each environment was created a possibility

of ten matchings using two in two mappings refer-

ring to this environment. The database generated by

gmapping is composed of mapping histories, and for

each mapping and each time period of one second (if

there is a change in map state), the state of the map-

ping at that instant is saved in image form.

The mappings were made by just one robot at a

time and the matchings were performed, using com-

binations between these mappings, and simulating as

if two robots were mapping at the same time.

After the generation of the database, for each en-

vironment, it inserts into the system implemented,

based on the method proposed in this work, two pro-

cesses of mapping at a time, in order to never repeat a

combination. Thus, there are ten possible matching

combinations for each environment, totalizing 100

matchings performed. This operation was performed

with the following conﬁguration of parameters in the

system:

• N = 30, number of corners to be extracted from

each map;

• q = 0.2, referring to the quality of the corners to

be extracted from each map;

• D

max

is half the greater distances among corners,

taking into account the two maps being matched;

• D

min

= 18, values in pixels;

• ε = 1.1, values in pixels;

• ν = 1.1, values in degrees.

The results were evaluated qualitatively and in the

cases of success a quantitative evaluation was applied.

In this quantitative evaluation, the accuracy of the

maps of each base environment was analyzed in com-

parison to the generated matching.

The teleop twist keyboard is present in the ROS and

through this at sends speed signals to the V-REP, which

in turn applies these signals to the robot. Available in:

http://wiki.ros.org/teleop twist keyboard

Map Matching for SLAM with Multiple Robots in Different Moments

215

4 RESULTS OF EXPERIMENTS

The qualitative analysis deﬁned only the attempted

matching to have succeeded or not. From 100 match-

ings performed with two mappings, 79 were success-

ful.

One examples of success can be seen in the Fig-

ure 8. The red color represents the mapping made by

robot 1, the blue color expresses the mapping done

by robot 2 and the green color represents the mapped

locations in common.

Based on the Figure 8 (a), one can observe the for-

mat of the real environment. Thus, in Figure 8 (b), it

is observed that the common areas, plotted by green

color, tend to have a higher concentration in neighbor-

ing regions, while as far as the green color goes. The

colors blue and red become more evident, demonstrat-

ing that, as it departs from the matching zone (green

regions), the error of each mapping, used in the pro-

cess, causes a loss of precision in matching. The same

phenomenon can be observed in Figure 8 (c) and (d).

(a) Map of the environ-

ment 1.

(b) Matching of the

mappings 1 e 2.

Figure 8: Representation of environment 1 and one of their

matchings.

The same process was applied to successful

matching results (between two different mappings of

the same environment), so that these results were

matched to other mappings, always being performed

two by two matching and thus providing a matching

among three or more mappings. Thus, some success

cases can be observed in Figure 9.

(a) Matching of the

mappings 1, 2 and

(b) Matching of the

mappings 1, 2, 3, 5

and 4.

Figure 9: Matchings with more than two mappings related

to the environment 2.

A quantitative analysis was performed with suc-

cess cases. In this analysis the comparison of the

original environments with the results of the match-

ings from which the number P

of black pixels and the

number P

of white pixels were extracted, which co-

incided (black with black, white with white) between

the original environment and the result of matching.

The number P

of black pixels and the number P

of white pixels present in the results of the matching

were also counted. Thus, for each obtained matching,

it was applied the Equation 3. From this formula, the

accuracy rate between the outcome of the matching

and the original environment was obtained.

(Pp + P

)

+ P

)

(3)

The accuracy of each individual mapping was cal-

culated. This calculation was performed using Equa-

tion 3, with the small change that the numbers P

and P

represented the values in relation to the indi-

vidual mapping, instead of matching results. In this

way, both individual mapping accuracies and match-

ing results were used to calculate the means, median

and standard deviation. These calculations were per-

formed for two classiﬁcations: the ﬁrst one represent-

ing only the individual mappings (thus representing

the maps generated via gmapping) and the second one

representing the results of the matchings.

Table 1: Quantitative result.

analysis Gmapping Proposed Method

Mean 0,929006 0,927027

Median 0,926777 0,931214

Standard Deviation 0,020205 0,021912

The quantitative results of matchings and map-

pings can be observed on the basis of Table 1. In these

results, it was achieved that the mean, median and

standard deviation differences were very small, being

less than 0, 01, proving that the matching method pro-

vides approximate results to the individual gmapping

mapping method. In addition, the averages and medi-

ans of precisions are considerably high (above 92%).

5 CONCLUSION

In this work, a mapping of SLAM maps with a sin-

gle robot model was carried out and the mappings

were performed at different times. These matchings

were carried out on two maps. In order to enable the

matchings, a technique of geometric relationship was

developed among the corners that were detected in the

mappings. This technique detected the three best cor-

ners for each two candidate points to ﬁt among the

ICAART 2019 - 11th International Conference on Agents and Artiﬁcial Intelligence

216

maps. After this detection, the attempt of matching

among these candidates is realized and then the gen-

erated maps are evaluated through a metric of similar-

ity.

The quantitative analysis of matchings expressed

the difference in precision between the mapping with

only one robot and the result of the matching do not

have considerable differences. Thus, matching can be

considered a viable tool for mapping processes with

more than one robot, but it remains to verify the navi-

gability of both maps, which should be done in future

work.

Through this method, good results were obtained,

in order to make possible future tests in real robots

and the continuity of the research. In order to improve

the results obtained, it is suggested to apply proba-

bilistic functions to verify if there is success or failure

in the matching process, in addition to trying to in-

crease the number of success cases.

ACKNOWLEDGEMENTS

The authors acknowledge FAPEMA, CAPES and

CNPq for their ﬁnancial support in the development

of this work. Special thanks to CEUMA, UEMA and

UFMA for their technical support.

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