Indoor Localization of the Points of Interest using RO-SLAM

Yamnia Rodríguez Esteves

, Julio Barbancho Concejero

and Antidio Viguria Jiménez

FADA-CATEC - Center for Advanced Aerospace Technologies, Seville, Spain

University of Seville, Seville, Spain

Keywords:

RO-SLAM, Beacons, Indoor Localization, RSSI, Sensor Network.

Abstract:

This paper describes an indoor localization system based on RO-SLAM technique, which has been led to

experimentation. The proposed system has been considered to have low power consumption. The nodes of the

network are based on the well-known architecture TelosB. A distance estimation study has been developed in

order to determine the relationship between radio power signal strength in a radio link and distance between

nodes. This relationship has a high variability and given its importance for successful tracking, much of this

work has been devoted to its study. This system could be applied as a usefull solution for localization in a

warehouse, where GPS-based system does not work. Based on the results obtained, this system is deemed as

feasible because the motes have been localized with an acceptable error (1.2 m) under real conditions.

1 INTRODUCTION

This work, on the one hand, evaluates experimentally

the accuracy of a particular estimation of the distance

from the power level of received signals using a sen-

sor network deployed with TelosB motes (J. Polastre,

2005), and on the other hand, it uses such estimations

as input to an implementation of the RO-SLAM al-

gorithm (Range-Only Simultaneous Localization and

Mapping) (F.R. Fabresse, 2013) to ﬁnd the coordi-

nates x and y for each device.

The organization of this paper is described as fol-

lows. First, the objectives of this work are described.

Second a panoramic of the distance estimation meth-

ods based on RSSI is addressed. From this study a

method of estimation, which will be led to experi-

mentation, will be selected. The whole description

of the system used is given in section named System

Study. Then, the evaluation of the estimated distance

calculation and the mote position estimations are ex-

plained in section Experimental Results. Finally, the

paper includes a section of conclusions and certain

approaches for future work.

2 OBJECTIVES

The main objective of this work is the localization of

multiple ﬁxed targets or markers, distributed in a com-

pletely unknown environment using a mobile system

Figure 1: SLAM problem diagram.

or robot which do not know its position.

An application of this approach could be the lo-

cation of goods (beacons) on a large warehouse (un-

known environment) taking advantage of a common

mobile system, such a truck cleaning, a crane, a trol-

ley, etc. In other words, the problem is to solve the

case in which a mobile system is in an unknown map

and an unknown position. When the system moves,

the system builds incrementally a map and determines

its position in this map.

This problem, as shown in Figure 1, can be solved

using different approaches. A booming technique in

recent years, with interest to research is to employ the

Rodríguez Esteves Y., Barbancho Concejero J. and Viguria Jiménez A..

Indoor Localization of the Points of Interest using RO-SLAM.

DOI: 10.5220/0005569400350042

In Proceedings of the 6th International Conference on Data Communication Networking (DCNET-2015), pages 35-42

ISBN: 978-989-758-112-0

 2015 SCITEPRESS (Science and Technology Publications, Lda.)

RO-SLAM method. An interesting article about RO-

SLAM is described in (J.L. Blanco, 2008).

The beacons or landmarks, which are shown in

Figure 1, are the characteristics of the map that pro-

vide a quantiﬁable measure of what has been the

movement of the system. For this purpose, it is possi-

ble to use a wide variety of sensors: lasers, cameras,

sonar, wireless sensor networks, etc.

The difference between SLAM and RO-SLAM is

based on the fact that, RO-SLAM solves this problem

considering that the information received by the sys-

tem is only the distance to the beacons. That is, the

system knows that it has a beacon at a distance ﬁxed

but the direction is unknown. SLAM also knows the

direction in which the beacon is located. The RO-

SLAM scenario allows the use of simpliﬁed sensors:

less sophisticated and therefore less expensive sen-

sors.

A frequent and cheap way to implement a distance

sensor is based in the use of wireless communications.

There are many manufacturers that provide this kind

of sensors. Therefore, to make a localization based

on the power level of the RF signals (Received Sig-

nal Strength Intensity, RSSI), the ﬁrst step is to have

a quantitative correlation between distance and power

level of a radio link. This essential ﬁrst step has re-

quired a big percentage of the time spent on the devel-

opment of the work described in this paper. The suc-

cess of the localization technique designed is based

on the accuracy of this step.

3 PANORAMIC OF RSSI VS

DISTANCE MODELS

This section collects a summary of the most impor-

tant models presented in the literature that relates the

strength of the radio signal received in a wireless de-

vice and emitted by a transmitter and the distance be-

tween them.

Currently, the RSSI propagation models in wire-

less sensor networks (WSN) include the model of free

space, the bidirectional ground reﬂectance model and

the log-normal shadow model or log distance path

loss model (J. Xu, 2010).

The free space loss (FSL) measures the spread of

the power in free space without obstacles. If the dis-

tance (d) is measured in meters and the frequency (f)

is measured in hertz, the formula of the FSL could be:

FSL(dB) = 20log

d + 20log

f − 187.5 (1)

In practice, the relationship between distance and re-

ceived signal power is more complex than the above

expression. Actually, the received power will be the

sum of a series of signals coming from different di-

rections, due to reﬂections objects and obstacles that

partially block the signal. Thus, the received power

resulting may be higher or lower than the output when

space free.

The ground bidirectional reﬂectance model is very

accurate when used in urban environment (J. Xu,

2010) but can not be applied to the context of this

work due to the heights of the antennas (below 50 me-

ters).

Furthermore, the log-normal shadow model is a

more general propagation. It is suitable for both in-

door and outdoor communications. The model pro-

vides a number of parameters that can be conﬁgured

according to different environments (J. Xu, 2010).

The model is usually expressed as the following equa-

tion:

L(dB) = P

+ 10nlog

(

) + X

(2)

Where L is the loss of power on the path, n is the path

loss exponent, d is the distance between transmitter

and receiver, X

is a Gaussian random variable with

standard deviation σ and P

is the received power ref-

erenced in the distance d

Over the years there have been a large number

of models to predict the path loss in typical wire-

less environments as large urban cells, small urban

cells, and more recently in buildings. These models

are mainly based on empirical measurements at dif-

ferent distances for a given range of frequencies and

in a particular geographical area or building. Exam-

ples of these models are the model Okumura, Hata

model or model of COST 231 (H. Rábanos, 2006).

All these models are complex for their application in

the present work, therefore the simpliﬁed model of

the log-normal shadow model is chosen for the exper-

imentation.

This simpliﬁcation is speciﬁed by Chipcon in (A.

Faheem, 2010). Chipcon is a transceivers manufac-

turer of the TelosB motes. The RSSI is given by the

following expression:

RSSI(dBm) = −10nlog

d + A (3)

Where n is the propagation exponent, d is the distance

from the transmitter measured in meters and A is the

strength of the received signal at a distance of one me-

ter. In this approach to the problem, RSSI and A pa-

rameters are known. In this way, n (4) can be cleared

and estimated as an average with each pair of (RSSI,

distance) collected in the experiment to ﬁnd a plau-

sible parameter value. Then, the distance (5) can be

cleared in order to calculate its estimated value ac-

cording every RSSI value. Finally, it is interesting to

DCNET2015-InternationalConferenceonDataCommunicationNetworking

Figure 2: System with UGV (Unmanned Ground Vehicle).

quantify the error with that approximation.

n = −

RSSI − A

10nlog

(4)

d = 10

−

RSSI−A

10n

(5)

This approach presents a problem of validation. Ex-

perimentally it is proven to be valid only beyond one

meter distance between transmitter and receiver.

4 SYSTEM STUDY

During the workﬂow, the system used for the study

has evolved. The systems used in chronological order

have been:

• Simulation system based on ROS (Robot Op-

erating System) (ROS, 2015) and MRPT (Mo-

bile Robot Programming Toolkit) (MRPT, 2015),

which estimates the positions of beacons using

RO-SLAM algorithms. Using this simulator, the

accuracy of estimated positions was tested versus:

– Number of beacons.

– Concentration of beacons.

– Error distance.

– Error odometry.

– Robot route.

• Actual system (Figure 2) formed by:

– an UGV (Unmanned Ground Vehicle),

– two TelosB motes with external antennas,

MTM-CM3000 MSP Rev 01 model (TelosB,

2015) and

Figure 3: System for RO-SLAM.

– the infrared positioning system, VICON (VI-

CON, 2006) as ground-truth

This system has been used to evaluate the esti-

mated RSSI/distance relationship.

• Actual system formed by:

– an autonomous car in scale,

– six TelosB motes with intern microstrip anten-

nas and other one with external antenna for the

reception.

– The VICON system as ground-truth.

This system has been used to evaluate the esti-

mated localization of the motes using the RO-

SLAM technique.

Thanks to the simulation system, three major con-

clusions are obtained:

• Odometry information is necessary in a RO-

SLAM system if you want an acceptable RMS in

position (e.g less than 5 meters).

• The concentration of beacons and path robot are

decisive. The best situation is a rich homoge-

neous concentration characteristics (shown in Fig-

ure 4(a)).

If at the beginning of RO-SLAM algorithm no in-

put data are obtained (as shown in Figure ??), it

will accumulate biggest error in the above situa-

tion. Similarly, the error decreases (uncertainty

of the beacon positions is reduced) more rapidly

as soon as the robot turn. Therefore, if the robot

does not rotate at the start of its travel, the ob-

tained location is worst (as can be seen in Figure

4(c)).

The VICON system is the most advanced optical mo-

tion capture system available. Determines the position of

the moving objects in the volume which controls with a mil-

limeter accuracy and very low latency (2.5 ms). Reﬂective

marks will be placed on those objects that we want to cap-

ture their movement. As shown in Figure 2, these marks are

added to UGV. In this manner, its position is known at any

instant and the distances to TelosB motes (whose positions

have also learned using VICON) can be calculated.

IndoorLocalizationofthePointsofInterestusingRO-SLAM

(a) Analysis of the number of beacons.

(b) Analysis of the arrangement of beacons.

Figure 4: Simulation results.

• Distance errors are obtained as far away surround-

ings [0.6,1.2] m under favorable conditions.

Based on these results, the actual experiments fo-

cus on a homogeneous deployment of beacons. This

way since t = 0, the robot receives signal transmitting

motes considering a reliable odometry.

All the software used in this study was designed

and implemented in C/C++ language. Including com-

munications systems, logging, conﬁguration and con-

trol. The control part refers to the drivers of mobile

platforms implemented in Matlab/Simulink

. Also,

the scaled RC car has been modiﬁed to have an au-

tonomous position control by adding a new design

hardware based on the board Raspberry Pi (Raspbery

Pi, 2015).

The software embedded on the motes manages

the communications between motes. This software is

based on the tutorial Demo RSSI (RSSI Demo, 2015).

The communication is conﬁgured to send data every

100 ms and the transmission power is not restricted

so that there is constant communication throughout

the testbed. The communication channel used in the

CC2420 transceiver is channel 26 (TinyOS toolchain,

2014) corresponding with the center frequency 2480

MHz. In this way, there are no problems of in-

terference between wireless communication (IEEE

802.11g) as shown in Figure 5 because the channel

13 is not used in the work environment.

Figure 5: No channel overlapping between WLAN and LR-

WPAN

5 EXPERIMENTAL RESULTS

In this section all results and analysis of the ﬁles

stored during the experiments are included. The pro-

cessing of these data was performed using the Matlab

tool.

It is needed to know that the CC2420 transceiver

of TelosB devices calculates the RSSI of 8 sym-

bol periods and stores the result in the record

Keep in mind that the division of channels in 802.11b

is the same as in 802.11g.

DCNET2015-InternationalConferenceonDataCommunicationNetworking

(a) RSSI measurements versus radio link distance.

(b) Chipcon model adjusted to the samples.

Figure 6: Received Signal Strength Intensity (RSSI) sam-

ples and generated model.

RSSI.RSSI_VAL. Texas Instruments speciﬁes the for-

mula 6 to calculate the received signal power (P) in

dBm.

P = RSSI_VAL + RSSI_OFF_SET (6)

The RSSI_OFFSET of CC2420 is empirically found

during development of the system. It is approximately

−45dBm (CC2420 Datasheet, 2007). The graphics

contained in the present document have that offset

added to the captured data during the experiments.

5.1 Results of the Distance Estimation

In Figure 6(a), it is possible to appreciate the

great variability of RSSI data and data discretization

(a) Ground-truth 1.

(b) Ground-truth 2.

Figure 7: Robot paths and mote locations.

(1dBm). This is the discretization of the sensor hard-

ware.

Given the variability of RSSI data for a same dis-

tance, a previous treatment is applied in order to min-

imize the data variability. The procedure to minimize

it is based on the calculus of the average of the data.

This is an approach used in various studies such as (J.

Xu, 2010). Furthermore, a 20th order data ﬁltered has

been made, as shown in Figure 6(a) to calculate the

unknowns Chipcon model.

The model obtained against the actual distance

based on the RSSI values is shown in Figure 6(b)

where the root-mean-square error (RMSE) is 0.9 m

in the distance range [1, 10] m. This is the validity

range of the approach. All these measures were col-

lected during the execution of the experiments shown

in Figure 7 at slow velocity (0.01 m/s).

Samples collected at high speeds (1 m/s) have

IndoorLocalizationofthePointsofInterestusingRO-SLAM

Figure 8: Experiment with constant obstacle in the radio

link.

been also analyzed, and except for the logical reduc-

tion of the samples, no signiﬁcant change in the pre-

vious ratio RSSI/distance is appreciated.

Furthermore, experiments have been conducted to

evaluate the effect of obstacles. First, by the transit of

a person in the LOS

and second, using a permanent

carton wall placed in the LOS of the communication,

as shown the Figure 8.

In the case of the person transit, given the mul-

tipath communication of the scenario and the obsta-

cle used in the experiment which does not obstruct

the entire Fresnel zone, the communication between

motes is not interrupted. But it is causing a decreased

level of received signal and therefore, the estimated

RSSI/distance using the relationship of the basic ex-

periment (same conditions but without obstacle) has

major errors.

When there is decrease in the received signal

strength, the estimation indicates a farther distance

between the motes when in fact they are closer. This

situation is caused by the fact that the communication

between nodes is occluded. This is shown in Figure

9, where a set of samples have been shifted to lower

RSSI values having the same distance from the basic

experiment.

In the case of carton box, the obstacle has no

impact on the Fresnel zone due to the absorption

of the radio signal by this material is insigniﬁcant.

Therefore, the collected samples during the experi-

ment does not reﬂect the existence of an obstacle in

the LOS.

Finally, experiments have been performed using

the technology 802.11g, where it has been evidenced

that the estimated model ﬁts the data worse (the RMS

error of the approximation increases) when the hard-

ware is changed.

Line Of Sight

Figure 9: Relationship RSSI/distance with intermittent ob-

stacle in LOS.

5.2 Results of the Localization

Estimation using RO-SLAM

In this section the estimation of the positions of the

motes for three conditions are evaluated:

• No odometry error (Figures 10(a) and 11(a)).

• Low odometry error, 0.001 m and analyzing only

the measures included in the validity range of the

estimated model (Figures 10(b) and 11(b)).

• Odometry error 0.01 m (Figures 10(c) and 11(c)).

In the most favorable situation (without odometry

error), three motes are perfectly located (RMS less

than a meter). If the mote disposition is observed,

these three motes coincide with the motes that are

within the route and therefore, available RSSI mea-

sures are over 360 degrees. The worst located motes

are far away from the robot path.

If the odometry error is increased to 0.001 m (by

adding a normally distributed variable error to the ac-

tual displacement), the location of the central motes

(5, 6 and 8) is worse but using the validity range of

the Chipcon model, distances between 1 and 9 me-

ters, the location of far motes is improved by a meter.

Finally, the rise of odometry error to 0.01 m

causes a signiﬁcant increase in RMS (several meters),

except in the mote 8 in which it remains close to one

meter.

Based on these results, it can be concluded that the

validity range of the model should be applied. It could

be even limited to the best modeled area [2.5,7]m.

Furthermore, a mote is better localized if the mobile

robot includes measures in all directions around it.

In the ﬁrst instance, this system is valued due to

the fact that the motes can be located with an accept-

able error (1-2 meters) under real conditions. An error

DCNET2015-InternationalConferenceonDataCommunicationNetworking

(a) Case: No odometry error.

(b) Case: Odometry error 0.001 m and measurements into

valid range.

Figure 10: Error evolution during the mote location estima-

tion.

(a) Case: No odometry error.

(b) Case: Odometry error 0.001 m and measurements

into valid range.

Figure 11: Actual mote locations and its estimation.

IndoorLocalizationofthePointsofInterestusingRO-SLAM

odometry 0.01 m can be assumed by sensors on the

market today. However, the system must be brought

into a more extensive testing campaign covering top-

ics such as obstacles.

6 CONCLUSIONS

Thanks to this work certain limitations and aspects of

the RSSI/distance relationship that have been applied

in the development of an indoor positioning system

based on RSSI measurements are known.

Talking about the distance estimation and the es-

timation of the mote positions, the results are promis-

ing. More data collection, reﬁnement parameters and

the application of techniques more advanced could

reduce the obtained errors and make the system ro-

bust to adverse conditions. The indoor localization

for most applications, such as navigational assistance

or ﬁnding property (about one or two cubic meters) in

a warehouse (100 or 200 m), an error of 1 or 2 meters

is acceptable.

7 FUTURE WORK

The central problem of this work is very wide and

can be extended along different paths and objectives.

Some of them are:

• Repeating the experiments in other different en-

vironments: free space, rich in metal scenarios,

communication between rooms, ﬂoors, etc.

• Study of the ﬁltering techniques and data fusion

as the radio link quality, LQI, for best results as

(S.J. Halder, 2012).

• Comparison with other wireless devices with dif-

ferent hardware and features.

• Greater experimentation with RO-SLAM, even

approaching it to a 3D location.

• Investigation of the obstacles effect and interfer-

ence on RSSI-distance relationship. Keeping this

relationship updated in an adaptive system using,

for example, known reference nodes (A. Awad,

2007).

• Research about the localization systems based

on the relationship studied, methodologies, tech-

niques, precision applications.

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DCNET2015-InternationalConferenceonDataCommunicationNetworking