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Indoor Pedestrian Navigation System Using a ModernSmartphone
Alberto SerraCRS4Parco Tecnologico, Edificio 1
Loc. Piscina Manna, Pula, CA, (Italy)+39 070 9250 230
[email protected]
Davide CarboniCRS4Parco Tecnologico, Edificio 1
Loc. Piscina Manna, Pula, CA, (Italy)+39 070 9250 303
[email protected]
Valentina MarottoCRS4Parco Tecnologico, Edificio 1
Loc. Piscina Manna, Pula, CA, (Italy)+39 070 9250 230
[email protected]
ABSTRACTIn this work we present a pedestrian navigation system
for indoor
environments based on the dead reckoning positioning method,
2D barcodes, and data from accelerometers and magnetometers.
All the sensing and computing technologies of our solution
areavailable in common smart phones. The need to create indoor
navigation systems arises from the inaccessibility of the
classic
navigation systems, such as GPS, in indoor environments.
Categories and Subject DescriptorsH.5.1 [Multimedia information
Systems]: Hypertext navigation
and maps.
General TermsMeasurement, Documentation, Experimentation, Human
Factors.
Keywords
Indoor navigation, dead reckoning, accelerometer, compass,
map.
1. INTRODUCTIONIn this paper, we propose the design and present
the early
development of an Indoor Navigation System based solely on
the
capabilities of a modern smartphone equipped with
accelerometers, compass, camera and Internet connectivity.
Indoor navigation can support commercial activities such as
the
research of products in a large mall, but can also be deployed
for
security reasons: evacuation of complex buildings, route
identification for visitors etc.
In the next section we describe the background and some
related
work in the field of indoor navigation systems. In the third
section
we introduce the prototype and present the preliminary tests
done.
The last section draws the conclusions and presents the
futuresteps to be done.
2. RELATED WORKIn [1] a method of personal positioning for a
wearable Augmented
Reality system is proposed, allowing a user to freely move
around
indoors and outdoors. In this system, users are equipped with
a
communication device with built-in sensors, a wearable
camera,
an inertial head tracker and display. The method uses the
dead
reckoning process to detect and measure a unit cycle of
walking
locomotion and direction achieved by analyzing the
acceleration
vector, angular velocity and magnetic vector acquired from
built-
in sensors (in this work is used the 3DM-G from MicroStrain
Inc).
The German Aerospace Center studies sensor fusion approaches
that combines GNSS (Global Navigation Satellite System),
foot
mounted inertial sensors, electronic compasses,
baro-altimeters,
maps and active RFID tags. The system consists of a
two-layer
sensor fusion architecture that operates with a Kalman
filter
where possible, and fuses other sensors and maps at a
higher-
level, lower rate, particle filter. In buildings, a few
dispersed
RFID tags can significantly improve the overall performance
of
the positioning system [2].
3. THE MOBILE PROTOTYPEDifferently from the above-mentioned
systems, our solution is
solely based on the capabilities of a common smartphone. The
data read from the phones sensors, combined with the
referencemap of the place, gives the actual position of the user
without
connecting to any external or pre-installed positioning
system
such as GPS, RFID, or WiFi trilateration using the dead
reckoning
technique. Dead reckoning is the process of estimating the
current
position of a user based upon a previously known position,
and
advancing that position based upon measured or estimated
speeds
over elapsed time and course. Errors occurring in the position
fix
are cumulative, growing with every step the user takes.
The prototype of this system, as mentioned in the
introduction,
uses the data from the motion sensors embedded in the
smartphone to compute the correct position of the user based on
a
known initial location. The smartphone application, still
under
development, is presented in figure 1. The user opens the
application and reads with the integrated camera a datamatrix
(2Dbarcode) placed aside the map of the floor (see figure 2).
Based on the URL encoded in the datamatrix, the application
downloads from a dedicated server the indoor vector map for
the
specific floor together with the initial position of the user on
the
map (corresponding to the point where the user stands when
scanning the datamatrix).
Copyright is held by the author/owner(s).
MobileHCI 2010September 7-10, 2010, Lisboa, Portugal.
ACM 978-1-60558-835-3/10/09.
Copyright is held by the author/owner(s).
8/3/2019 Paper Mobile Hci 2010
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Figure 1. Screen of the application with a pedestrian route
example.
When the user starts to walk, the application draws step by
step
the position of the user, as a continuous line, over the
downloaded
map of the building floor.
The application tracks the number of steps taken by the user
basedon the data generated by the smartphones accelerometers. A
single step is detected for each couple of consecutive
negative/positive peaks in the acceleration values, i.e. a
zero-
crossing of the normalized signal generated by the
accelerometer.
The current orientation of the user is measured by the
smartphones digital compass (the parameter 'Orientation' in
Figure 1). The initial orientation is set when the user scans
the 2D
barcode, being perpendicular (within a certain angle) to the
floor
plan. The relative position of the device with respect to the
user
(e.g. in a pocket) does not influence the dead reckoning
estimation. If the device is held in front of the user, the
magnetic
compass provides the step-by-step heading improving the
overall
accuracy of the positioning method.
Figure 2. User reads a datamatrix to download the map and
his start position.
3.1 Experimental tests and resultsBefore starting the
application, the compass needs an accurate
recalibration. This recalibration is necessary because the
compass
is subject to several errors: initially it has an inaccuracy
of
maximum 5 degrees, depending also on the used device and on
the presence of electromagnetic interferences.
The step counter module based on the accelerometer data was
tested and validated after thorough tests, performed in an
indoor
environment using both men and women with different physical
features. The mean placement error was 3,8% on a series of
20
runs consisting of an average step count of 40 steps.
The application based on the compass and the step counter
modules, was able to detect accurately both orientation and
displacement of an user in an indoor environment, for short
runs
(less than 100 m).
4. CONCLUSIONS AND FUTURE
WORKIn this paper we proposed a method for a pedestrian
indoor
navigation system. We developed this application on a modern
smartphone and did first experiments in a real indoor
environment, measuring the encountered errors.
Future work will include the improvement of the measurement
method of the walking steps to overcome the shortcomings of
the
current-used fixed-value step length. The estimation of the
step
length could be obtained by the strength of the step
acceleration
movement (through a probabilistic algorithm) and the
personal
information data written previously by the user. As seen in
the
experimental phase, the step counter is subject to
accumulated
errors, raising the need of a fixing algorithm such for example
a
particle filter or a Kalman algorithm [3].
The knowledge of walls, doors, pillars and other elements can
be
also used for fixing the position errors if these elements
are
already included as vector data in the floor map, which in
this
case could be an SVG image.
An alternative error correction method might be the use of
the
integrated smartphones camera. The images taken live by the
camera could be segmented in order to partition the image
into
relevant regions. These regions, a simplified representation of
the
acquired image, can be more meaningful and easy to parse.
Usingthis technique, and using isometric maps of the flat building,
it is
possible to compare, looking for similar features, the stream
of
live camera images and the map to get the correct indoor
location,
fixing the previous accelerometer and compass errors.
5. REFERENCES[1] Kougori, M. and Kurata, T. 2003. Personal
Positioning based
on walking locomotion Analysis with self-contained sensor
and a wearable camera. In Proceedings of ISMAR2003,
103-112
[2] Krach, B. and Robertson, P. 2008. Integration of Foot-
Mounted Inertial Sensors into a Bayesian Location
Estimation Framework. In Proceedings of 5th Workshop on
Positioning, Navigation and Communication 2008 (WPNC
2008), Hannover, Germany.
[3] Woodman, O. and Harle, R. 2008. Pedestrian Localisation
for Indoor Environments. In Proceedings of the 10th
International Conference on Ubiquitous Computing
(UbiComp), Seoul, Korea, ACM 2008, 114-123
