AUTOMATED 2D MEASURING OF INTERIORS USING A MOBILE

PLATFORM

Alexander Fietz, Sebastian M. Jackisch, Benjamin A. Visel

Robert Bosch GmbH, Corporate Research, 71701 Schwieberdingen, Germany

Dieter Fritsch

Institute for Photogrammetry, Universitaet Stuttgart, 70174 Stuttgart, Germany

Keywords:

Mobile robots, Surveying, Indoor environment, Exploration, Scan matching.

Abstract:

This paper presents an approach for a fully automated measuring of a self-contained indoor scene in 2D using a

mobile platform. A novel sensor is used, designed for the acquisition of precise ﬂoor plan scans, mounted on an

ordinary mobile robot. The software framework is conﬁgured to optimize the accuracy and the completeness

of the acquired data. Therefore an exploration strategy is presented that ﬁnds gaps in the data and determines

the next best view in an explorative way. The scene is presented to the system as continuous line segments.

Data management builds up the gathered point cloud in a homogeneous density, using the relevant and most

accurate available information. Existing scan matching techniques are modiﬁed in a way to work robust and

precise in the scenarios expected. The framework is tested in an exemplary indoor scene. Additionally, the

scene is surveyed with a referencing system to build ground truth data and to enable an accuracy evaluation of

the developed system.

1 INTRODUCTION

Architects, craftsmen but also further occupational

groups engage in the constructional modiﬁcation of

interiors. Often the basis of their work is a de-

tailed and accurate ﬂoor plan. The acquisition of the

necessary geometrical information plus the following

modeling still is a time-consuming procedure. Typ-

ically, the measuring instruments used therefore are

tachymeters or just measuring tapes. Thus an auto-

mated ﬂoor plan creation is a topic that is worth in-

vestigating.

The intention of building a ﬂoor plan at the push

of a button requires the utilization of a mobile robotic

platform. This issue implicates a number of funda-

mental tasks that have to be overcome. The existence

of a hardware framework to allow processing like the

activation of sensors or the wheels is just a basic re-

quirement. For an autonomous operation the system

has to be able to locate itself in an unknown environ-

ment based on the available sensor data. Simultane-

ously one or even several maps have to be built up out

of the collected data. The generation of a ﬂoor plan

further used by the operator, but also a representation

of the environment the robot can deal with. Normally

these are associated with each other. The system has

to know which parts of the environment still have to

be surveyed and how it manages to do that. There-

fore exploration strategies have to be applied that pro-

pose new measurement positions and determine paths

to ensure a collision-free navigation.

The paper is organized as follows. Section 2 ad-

dresses the related and previous work, section 3 in-

troduces the hardware framework of the used system.

In sections 4&5 the scan matching technique and the

exploration strategy are presented. Experiments and

results are given in section 6, followed by section 7

that concludes the paper and refers to future work.

2 RELATED WORK

There has been a lot of research work in building two

dimensional maps using robotic platforms. Thereby

a broad variation of the presented approaches can be

found, according to the already mentioned number

of tasks a robotic system must accomplish. The ad-

equate representation of the environment is one of

115

Fietz A., M. Jackisch S., A. Visel B. and Fritsch D. (2010).

AUTOMATED 2D MEASURING OF INTERIORS USING A MOBILE PLATFORM.

In Proceedings of the 7th International Conference on Informatics in Control, Automation and Robotics, pages 115-120

DOI: 10.5220/0002877201150120

 SciTePress

the interesting subtopics. Depending on the systems

major task an occupancy grid (Elfes, 1987), feature-

based (Kuipers et al., 1993) or topological (Choset

& Burdick, 1996) maps can be the most appropriate

choice. Occupancy grids have the nature of an easy

handling, but need a lot of memory capacity. Thus

their usage comes along with an increased computing

time, especially in spacious applications. In compari-

son to that, feature-based and topological maps allow

a rapid treatment, but depict just an abstraction of the

scene.

Exploration strategies are based on these repre-

sentation types. An early approach was given by

Yamauchi in [1997]. He used a frontier-based ap-

proach that is based on an occupancy grid and accom-

plishes the exploration by classifying single grid cells.

Gonz

alez-Ba

nos & Latombe presented in [2002] a

similar approach, that also observes the frontiers to

the unknown areas, but directly evaluates the mea-

sured data points. Consecutive data points are con-

nected to line segments and the obscurations inbe-

tween are ﬁlled by modeling with geometrical primi-

tives. The resulting line-like frontier models are built

for every new scan and update the global model with

a mergence. An alternative approach is given by

Schmidt et al. in [2006]. The assumption, that the en-

vironment consists of simple rectangular geometries

enables an abstraction of the collected data to geomet-

rical primitives. Exploration is pushed by checking

their topology and a sufﬁcient observation of them.

These strategies do not suit to the aspired application

because they either primarily don’t target the com-

pleteness of data or can’t handle more complex indoor

geometries. So an alternative method had to be found,

that is presented in chapter 4.

Exploration applications require a simultaneous

map building. Thereby, the well-known SLAM-

problem, the bilateral dependancy of data registra-

tion and auto-localization, has to be considered to

deal with the geometrically imprecise maps as well as

noisy and ambigious sensor information. Various ver-

sions, how scan data can be matched to a map, were

presented in the past years (Besl & McKay, 1992),

(Lu und Milios, 1994), (Yaqub et al., 2006). State of

the art in global mapping is the usage of probalistic

methods. For robot motion and data acquisition un-

certainty models are generated at which Kalman or

particle ﬁlters are applied. In (Thrun, 2002) a general

review is given.

3 HARDWARE FRAMEWORK

The applied laser scanner is a prototype, designed for

precise indoor measuring tasks. Within a measure-

ment operation a point cloud of 3600 points can be

recorded that yields a 360

◦

view of the environment.

The combination of multiple phase measurements al-

lows distance measurements with a standard deviation

of a few millimeters in a range of 7.5 meters. An in-

tegrated leveling unit adjusts the ﬂoor uneveness to

make horizontal measurements possible. Signal anal-

ysis allows a prior ﬁltering of erroneous data.

The sensor is mounted on a custom three-wheeled

VolksBot platform provided by the Fraunhofer Insti-

tut (Fraunhofer, 2009). Data handling and sensor con-

trol is done with a notebook placed on the top of the

platform. This set-up minimizes obscurations for the

sensor. Used algorithms are implemented in Matlab

(Figure 1).

Figure 1: Hardware set-up.

4 GAP FILLING EXPLORATION

This section introduces an approach for the explo-

ration of an indoor scene in order to provide data of

the whole environment in sufﬁcient density.

A: Representation of the Environment. The ap-

proach is based on the assumption that all occur-

Figure 2: Representation of the environment: a) mea-

sured line fragments l

, b) continuous line segments

ICINCO 2010 - 7th International Conference on Informatics in Control, Automation and Robotics

116

ing surfaces can be described as continuous and self-

contained line segments L

(Figure 2b)). The envi-

ronment is assumed as static. During the measur-

ing procedure exploration is incomplete and actually

searched segments L

are just existent in fragments l

that have to be connected (Figure 2a)). A segment

ends, when no more neighboured points can be found

within a certain distance d

gap

. Since a phase measur-

ing sensor is used, the layout of the measuring and

exploration procedure has to be adapted accordingly.

Data can be captured just from single selected points,

while the mobile platform is resting. This causes a

typical next best view problem.

For the deﬁnition of the area, accessible for the

robot, an occupancy grid is used, composed of clas-

siﬁed cell elements o

m,n

(Figure 5(a)). Spatial areas

infused by the laser beams of the sensor can be clas-

siﬁed as accessible, respectively as buffer area, if the

affected cells are arranged close to obstacles or unex-

plored regions. This is necessary because the robot is

regarded as a circular, moving item in the map. The

same grid is used for path planning. If the next mea-

surement position is determined, an A* Algorithm is

used to ﬁnd an unobstructed path consisting of pass-

able grid cells to it.

The end points of l

refer to gaps g(l

)

1|2

in the

logged data. Since these should be eliminated dur-

ing exploration they are the basic information of the

strategy. Existing approaches assume a knowledge

of the assignment U(l

↔ l

) between single gap

points building an unexplored area (Gonz

alez-Ba

nos

& Latombe, 2002). This is advantegeous for the

choice of explorative measuring positions, but leads

to ambiguities if the scene consists of several line seg-

ments L

. Hence, the presented approach tries to de-

termine U(l

↔ l

), but isn’t reliant on it.

B: Score Map. Potential measuring positions p

m,n

arise from the set of accessible grid cells p ∈ M with

M = {p

m,n

| c

m,n

b= accessible}. All candiate positions

are evaluated due to their explorative suitability S

follows:

= − f

· k ·D

− f

∑

i=1

+ f

∑

i=1

(1)

with k: number of visible gaps ∈ p

m,n

The candidate position with maximum value S

elected for next measurement position. To avoid un-

necessary long distances between the single measure-

ments, the euclidean distance D

between the current

cur

and the next position candidate x

pos

is considered.

Long distances lead to low evaluations. The same is

true for the distance D

of x

pos

to the visible data

gaps (see section C). Since short distance measure-

ments promise a better signal-to-noise ratio, those are

prefered. Another inﬂuencing factor describes the im-

portance of a data gap with regard to the progress of

the exploration (see section D). Here, an assumption

of gap assignments is utilized to determine the size of

the unexplored area the gap could belong to. Partial

inﬂuences are weighted individually with the factors

- f

This results in a score map, that can be overlayed

with the current occupancy grid. (Figure 5(c)) shows

this exemplarily. Bright shaded grid cells indicate a

good suitability for being the next measurement posi-

tion.

In the course of the exploration uncloseable data

gaps can occur, caused by unobservable surfaces or

adverse geometrical situations. These must not hin-

der the exploration procedure. The system has to be

able to recognize this circumstance to adapt its behav-

ior appropriately. For that reason, data gaps are pro-

vided with counters. A gap, that couldn’t be closed

after a certain number of trials, is not tried to be ob-

served any longer. That provides, that in repeatedly

observed areas these data points are contained, which

were captured from a shorter distance. Thus an inher-

itance of gaps must be intended for the case of a data

exchange.

Figure 3: Visibility of gaps.

Figure 4: Area of visibility.

C: Area of Visibility. To ascertain the visibility of

gaps, inverse visibility scans are performed. The en-

vironment of each gap is divided into radially extend-

AUTOMATED 2D MEASURING OF INTERIORS USING A MOBILE PLATFORM

117

ing zones. Data gaps are visible along each zone up

to the point a line segment l

intersects the zone or the

sensors maximum range is reached (Figure 3).

Gap visibility is conﬁned additionally. On the

one hand, the point of view, from where gaps can

be observed, should not have a too shallow angle of

incidence regarding the surface normal, because of

signal reﬂection. On the other hand measuring po-

sitions should be chosen, that imply a progress of

global exploration. Therefore, a ring segment-shaped

area of visibility AoV = AoV (lsd,[α

,α

],[d

]) is

deﬁnied, from where gaps can be observed (Figure 4).

AoV is aligned to~n the normal vector of lsd, the local

segment direction of the gap . To avoid object ob-

servations on the back side, ~n points at the same side

where points of l

where measured from. To enable

an explorative behavior, AoV is spanned in an area

beyond~n relative to the current robot position.

D: Assignment Estimation. Gap assignments

U(l

↔ l

) are not used directly to deduce advanta-

geous measuring positions. Nevertheless, it is tried to

estimate potential connections. At least conclusions

on the size of the unexplored region, the data gaps

belong to, shall be retrieved to obtain an indicator

for their importance with regard to the explorative

beneﬁt. So possible mismatches just lead to adverse

ratings instead of fatal misbehaviour. The estimation

is made using the hungarian method (Kuhn, 1955).

Starting point is the ﬁlling up of the square cost

matrix C

n×n

, that displays a complete bipartite graph.

Assignment costs c

i, j

between the data gaps g

n,1|2

are

given with their distances to each other. Distances of

assigned gaps are integrated in equation 1 as D

i, j

(

| g

− g

|, i f l(g

) 6= l(g

∞ else.

(2)

So gaps g

n,1|2

, belonging to the same line segment l

should not be assigned to each other. This assumption

is valid as long as there are no almost completely ex-

plored line segments L

, that could be closed. Then

the probability of mismatches is increased. Figure

5(b) shows this exemplarily.

5 SCAN MATCHING

When the next measuring position is chosen the sys-

tem has to go there. The scanner is just usable at

standstill, so odometry is the only accessible data for

motion control. In interiors its accuracy is sufﬁcient

for collision-free navigation, but not for a direct reg-

istration of collected data, in particular when highly

precise models are to be derived. Hence a robust and

precise scan matching is necessary for updating the

current system position and to assure exact mapping.

Registration of two-dimensional point clouds can be

described as a rigid body transformation on the ba-

sis of 2 translative components T

, T

and a rotatory

component α. So a point P

can be transformed with:







cos(α) −sin(α)

sin(α) cos(α)











(3)

To estimate the unknown transformation parameters

∆

ˆx

an adjustment according to the Gauß-Markov

model is proceeded:

∆

ˆx

= (A

GA)

−1

G∆

per





∆





(4)

A detailed description can be found in (Niemeier,

2002). Functional relationships f

between scans

have to be derived. For that perpendicular distances

∆

per

between data points P

of a new scan D

new

and

their foot points F

on the straight line segments s,

detected in the l

of the existing data D

exs

, are used.

s are derived like in (Nguyen et al., 2005). To derive

, ∆

per

is expressed using equ.3:

∆

per,n

=| P

− F

| (5)

Matching should take place in a manner that

∑

∆

per

minimized. Since f

are not linear functions, approx-

imate values have to be found and the adjustment has

to be executed iteratively.

For A

n,3

, f

have to be differentiated with respect to

the unknown parameters x:

n,3







1,1

1,2

1,3

2,1

2,2

2,3

n,1

n,2

n,3







with : a

n,u

= (

δ f

δx

) (6)

To give longer s a higher priority, since they are

less affected by outliers and signal noise, every f

is weighted on the trace of the weight matrix G, de-

pending on their own length and the longest occurring

straight line segment:

(n,n)

| s

max

(7)

Two essential thresholds affect the matching. First,

line

for the deﬁnition of s, designating the maximum

permitted distance of points to the best ﬁt straight line.

Second, d

area

for the area on both sides of s, where

points of D

new

can be found for ∆

per

(Figure 6). Points

further aﬁeld are not considered.

One major difﬁculty in scan matching is the

ICINCO 2010 - 7th International Conference on Informatics in Control, Automation and Robotics

118

(a) Occupancy grid. (b) Gap assignment. (c) Score map.

Figure 5: Score map determination.

Figure 6: Scan matching with iterative stronger

threshold restriction: 1) d

area

, 2) straight line segment

s, 3) perpendicular disances ∆

per

, 4) new scan data

new

, 5) existing scan data D

exs

choice of adequate thresholds. While weak thresholds

entail a lower accuracy, a more strictly selection is at

the expense of robustness. This led to the implemen-

tation of an iteratively exerted matching, where d

line

& d

area

are restricted increasingly by halving. This

is iterated as long as there can be found enough line

segments in D

exs

with a certain minimum length and

a sufﬁcient variation in their orientation. The latter is

necessary to avoid a degree of freedom in any spatial

direction. In this way it is achieved that just straight-

est lines and the points in their closest neighbourhood

are used for matching.

6 EXPERIMENTAL RESULTS

The system setup was tested in a interior scene of

ca.150 m

, consisting of several rooms and cleared

out (Figure 7). Based on 93 measurements a point

cloud of around 15k points was drawn up. Thereby

the system covered a distance of just over 160 meters.

Parameters and thresholds according to Table 1 were

used.

Table 1: Parameter settings for test environment ex-

ploration.

Parameter Value Description

area

(ini-

tial)

5cm distance to s, where points for ∆

per

are con-

sidered

line

(initial) 2cm maximum distance of points to the best ﬁt

straight line

1.3 weighting of the distances to p

m,n

1 weighting of the distances to g(l

)

1|2

0.6 weighting of gap assignment assumption

gap

2cm minimal data gap distance b= model density

grid

0.1m edge length of grid cells o

m,n

counter

3 maximum number of trials to eliminate a gap

5 angular intervall for area of visibility

60 angular intervall for area of visibility

0.3m spacing intervall for area of visibility

7.5m spacing intervall for area of visibility

In principal, exploration of all rooms was success-

ful. Deﬁcits can be observed with regard to the efﬁ-

ciency. One issue are seesaw changes between zones

interesting for exploration, e.g. measuring positions

10, 11 & 12. Also notably is the high amount of mea-

surements on the left side of the scene. This was

caused by ﬁnely structured heatings, that were in-

stalled there. These led to many obscurations in the

scanner data and thus to data gaps the system tries to

close. To be able to conclude about the systems accu-

racy, ground truth data were recorded by generating

a model of the same scene with a tachymeter

. Same

points were tapped manually from the model the mo-

bile system generated. Both models were overlayed to

ascertain difference vectors. Coordinate differences

possess a standard deviation of s

= 5.2mm with max-

imal deviations of 1.2cm (Figure 7f)). As one can see,

the alignment of vectors shows no signiﬁcant system-

atic inﬂuence of errors. d

area

& d

line

in Table 1 and in

the example of Figure5 differ. As Figure5 is for illus-

trative purpose, thresholds are set stricter in practise.

A rough pre-matching step is made then to overlay the

data already relatively accurate for higher robustness

against errors. Therefore the algorithm of (Winkel-

Leica TPS400 accuracy: -distance: 2mm + 2ppm -angle: 5

AUTOMATED 2D MEASURING OF INTERIORS USING A MOBILE PLATFORM

119

Figure 7: Test environment: a) model data, b) differ-

ence vectors to tachymeter model, c) path, d) succes-

sion of measuring positions, e) background, shaded

dependend on the point of time, the area was declared

as passable, f) histogram of differences to tachymeter

model.

bach et al., 2004) was adapted to the present condi-

tions.

7 CONCLUSIONS

This work introduces an approach for automated ex-

ploration and mapping of interiors. Experiments

showed that the system can deliver ﬂoor plan data of

high accuracy. To our best knowledge no previous

work on robotic 2D mapping could prove a compa-

rable accuracy. This is attributable to the sensor, but

also to the applied matching method. Mapping and

localization is done via scan matching solely, in con-

strast to many current approaches. So spacious en-

vironments cause difﬁculties for the approach, since

e.g. loop closing scenarios (Stachniss et al., 2004)

are not considered here. But the approach is demon-

strably powerful as long as the interiors size is limited

and there are enough geometrical landmarks existent

to deliver constraints to the matching, what both is the

case for small ofﬁce environments. The exploration

strategy presented allows a complete observation of

the scene, but its efﬁciency is improvable. So future

work will engage in implementation of higher-level

strategies for exploration planning and control.

REFERENCES

Besl, P.J. and McKay, N.D. (1992). A method for registra-

tion of 3D shapes. IEEE Transaction on Pattern Ana-

lyisis and Machine Intelligence 14, Nr. 2. p. 239−256.

Choset, H. and Burdick, J. (1996). Sensor based motion

planning: The hierarchical generalized voronoi graph.

Ph.D. Thesis California Insitute of Technology, Pas-

sadena.

Elfes, A.(1987). Sonar-based real world mapping and

navigation. IEEE J. Robotics and Automation, RA-

3(3):249 − 265.

Fraunhofer IAIS Projekt: VolksBot. Domain:

http://www.volksbot.de/ (29.09.09).

Gonz

alez-Ba

nos, H.H. and Latombe, J.C. (2002). Naviga-

tion strategies for exploring indoor environments. Int.

J. of Robotics Research. 21(10–11):829–848.

Kuhn, H. W. (1955). The hungarian method for the assign-

ment problem. Naval Research Logistic Quarterly, 2

83–97.

Kuipers, B., Froom, R., Lee, W.K. and Pierce, D. (1993).

The semantic hierarchy in robot learning. In J. Connell

and S. Mahadevan, editors, Robot Learning, Kluwer

Academic Publishers, Boston, MA.

Lu, Feng; Milios, Evangelos (1994). Robot pose estima-

tion in unknown environments by matching 2d range

scans. IEEE Computer Vision and Pattern Recognition

Conference (CVPR).

Niemeier, W. (2002). Ausgleichungsrechnung. de Gruyter,

Berlin - New York, ISBN 3-11-014080-2.

Nguyen V., Martinelli A., Tomatis N. and Siegwart, R.

(2005). A comparison of line extraction algorithms us-

ing 2D laser rangeﬁnder for indoor mobile robotics.

Proc. Conference on Intelligent Robots and Systems,

Aug 2005, Edmonton, Canada pp. 1929–1934.

Schmidt, D., Luksch, T., Wettach, J. and Berns, K.

(2006). Autonomous behavior-based exploration of

ofﬁce environments. 3rd International Conference on

Informatics in Control, Automation and Robotics -

ICINCO, Setubal, Portugal, August 1-5, 2006, pp.

235-240.

Stachniss, C.,Haehnel, D. and Burgard, W. (2004). Ex-

ploration with active loop-closing for FastSLAM.

In:Proceeding of 2004 IEEE/RSJ International Con-

ference on Intelligent Robots and Systems. Sendai,

Japan, pp.1505–1510.

Thrun, S. (2002). Robotic Mapping: A survey. Technical

Report CMU-CS-02-111, School of Computer Sci-

ence, Carnegie Mellon University, Pittsburg

Winkelbach, S., Rilk, M., Schoenfelder, C. and Wahl, F.M.

(2004). Fast random sample matching of 3d frag-

ments. Pattern Recognition, 26th DAGM Symposium.

Volume 3175 of Lecture Notes in Computer Science.,

Springer p. 129-136.

Yamauchi, B. (1997). Frontier-based approach for au-

tonomous exploration. In: Proceedings of the IEEE

International Symposium on Computational Intelli-

gence, Robotics and Automation, pp. 146-151.

Yaqub, T., Tordon, M.J. and Katupitiya, J. (2006). Line

segment based scan matching for concurrent map-

ping and localization of a mobile robot. In: Proc.

9th International Conference on Control, Automation,

Robotics and Vision, Singapore, pp.1-6.

ICINCO 2010 - 7th International Conference on Informatics in Control, Automation and Robotics

120