personal navigation system

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Proceedings oi 2004 IEEElRY International ConferenceonIntelligent Robots and SystemsSeptember 28 -October 2,2004, Sendai, Japan

Personal Navigation SystemJari Saarinen,Jussi Suomela, SeppoHeikkila,Mikko Elomaa and Aame HalmeHelsinki University of Technology

Automation Technology LaboratoryPLSSOO,02015 HUT, Finland.Email: [email protected]

Abstract - This paper presents a human dead-reckoningsystem far beaconless indoor positioning. The system isbased on traditional dead-reckoning Senson like compass,gyro, and accelerometers. Due to the difficult kinematicsof a human, there are no ready solutions fo r theodomehy. This problem is solved by using a self-madestride length measurement unit and laser odomehy. AUthe sensors are Integrated to a complete system includingsensor fusion. The fnnctiondityof the integrated systemisverified with tests io an office environment. Finally thetest results are analyzed.

Keywords - Pprsonal Navigofion; indoor localisation:dead reckoning.I. INTRODUCTION

The search and rescue type of missions have gainedincreasing attention in the past years. Utilizing roboticsin these missions can decrease the execution time of themission and save human lives. The fire-fightingscenario is one of the most common rescue situations.Despite of their name the fr e fighters are not alwaysfighting against the fue; most of their tasks are sortingout false alarms i.e. they check the alarming building inorder to fmd out what sensor in which area gave thealarm and was there a real reason for the alarm. Othertypical searching and mapping missions forfire ightersare accidents with leaking gases or liquids and - ofco me - the traditional smoke diving in a bumingbuilding.

Presently a typical fire-fighting scenario includesseveral lire-fighter pairs and a mission coordinator. Thecwrdinator stays outside the building and givesinstluctions via a short wave transmitter. Usually thereis a rough map of the building in hand, but not adetailed one. As in many other applications the moreknowledge can be got from the situation the better.According to tbe 6re fighters, the knowledge of thepositions of the fire fighting crew is crucial both for thesafety and the mission control. For localization, the6ame of reference is also needed and in some casesthere is no a priori map available for the mission. Thismeans that the localization system has to have thecapability to use an existing map of the building or tomap the unknownenvironment during the mission.

European Community supported project "PeLoTe"(Building Presence through Localization for HybridTelematic Systems) is studying a search and rescueconcept based on cooperative human and robotic

entities. The aim of the project is to map a totally orpartially unknown environment by human and roboticexplorers and generate a common map - presence -fiom the mapped data. One of the main topics islocalization, which is the common denominator for theboth entities in order to generate the feeling ofpresence.The accurate position of all crewmembers on a mapgives a clear view of the situation to the operator. Theexploring entitieswillknow the position of the others aswell as their own and-before all - an accurate positionmakes it possible to map the environment. The a priorimap, position of all entities and the real-time mappingdata are generated to a model or a representation of theenvironment. Both the robots and humans can use thisrepresentation for navigation and updating their sensorinformation.

The challenges of the work are how to generate acommon presence, which both humans and robots canunderstand, how to map the environment and finallybow to localize a human without a priori map or readyinstalled beacons. [I ]Il. PERSONAL NAMGATION SYSTEM

The Personal Navigation system (PeNa) is a systemfor localizing a human in indoon. The system calculatesthe position from an initial position without using a mapor external landmarks. Thus the PeNa system m thecurrent state can be referred in robotics terms as a dead-reckoning system. The system does not use any extemalinformation sources for the position update. At themoment a map is neither needed nor generated during amission. Therefore the performance of the system istime dependant; the longer the mission continues thepoorer is the position accuracy (i.e. the positionuncertainty isunbounded).

The PeNa hardware (see fig.1 and fig. ) includes:batteries, power conversions, Stride LengthMeasurement Unit (SiLMLI), a fiber optic gyro, acompass, SICK laser scanner and a laptop. All thehardware is mounted in a backpack. The laptop isinstalled in the front to serve also as a display for userinterface.

The existing PeNa hardware is a demonstrationalprototype, thus the outlook should not be considered forthe real firefighting conditions.


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Figure 1.PcNa squipmcntm. HARDWARE

.The backpack is a standard biking equipment. TheFrame is made of aluminum. The solid h m e is neededin order to maintain the same frame of referencebetween the different sensors. The solid Frame alsosupports the equipment and thus the load is easier tocarry. The total weight of the system is approx. 14kgwithout the laptop.

The power subsystem consists of two 12V 4Ahlead-acid batteries connected in serial to provide 24VDC . The average power consumption of the PeNasystem is less than 30W.

Figure 2. T h e backpack and the hmdwweThe gyro in PeNa system is Hitachis Fiber OpticGyroscope HOFG-X. The gyroscope measures theangular velocity of the beading angle. There is no

compensation between the other axes. This means thatthe gyroscope bas to be installed as horizontally as

possible. While walking, the body of the gyro iscontinuously swinging around the all three axes due tothe human movement. Thus, the gyro is not measuringexactly in the correct plane, which generates extra driAto the beading estimate.

The Laser scanner is the SICK LMSZOO. The laseris measuring distances in 2 D plane. The device is set tomeasure a 180degree sector with 0,5 degree resolution.The compass is a 3D orientation module, with 3 Dmagnetometers, 3 D gyros and 3D accelerometers. Themodule is used as a gyro compensated compass.The self-made Stride Length Measurement Unit

(SiLh4U) measures the distances between the ankles.SiLMu is an ultrasound-based device that measures thedistance based on Time-of-Flight. The lengthmeasurement is done continuously at 60Hz rate. Theultmsonic transmitter is placed on the other foot andreceiver to another (see Fig. 3). Microcontrollercalculates the distance between the legs and stores thevalue to the memory. The value is constantly requestedby the CPU, which makes tbe higher-level calculations.Reflectors are used in order to widen the transmittingand receiving sectors. For the stride length measurementa beam angle of almost 180 degrees is needed. Thereflectors are done from one half of a cone, cut in twofrom top to bottom (Fig. 3).

Figure 3. T h e Stride Lmgth Mcawemml unitAll the sensors are communicating via serial ports.There is an own thread for each sensor and thesynchronization of the data is done by using timestamps

and buffering. The fact that the data is accessed througha serial port causes some time differences. However,using locks in the code bas minimized the crucial timedifferences.

IV. METHODSA . Heading estimation

The heading is estimated independently by fusingthe compass and the gyroscope measurements. Thefusion is done with a Kahnan filter. In indoors theelectric fields and steel structures causes disturbances tothe magnetic field. Thus in short term use the compasserror can be up to k40 degrees. The fiber optic gyroprovides a good short-term accuracy, but is influencedby drift in the long-term use. The typical drift of thegym is approximately 2 degimiu including also theadditional drifis caused by the movement of therotation axis duringwalking.

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complete step is held as a reference and the upper bodylocation is appro-&nated with half of the perpendiculardisoncc to the other foot using (5) .

r q / Z r m , : -mi":m,,&j/e = ( 5 )2The reference point IS held on the foot that touchesthe ground. The reference point changes when a new

completc step is recognized.The position estimate is cdculolcd by using aKalman filtered heading md stride length

measurements. The main idea is that Ihe heading isfixed when the human has both legs on the floor. Thishappens when the step has been taken. The newlocation is calculated fu r each angle measurement byfinding nearest stride measurement. If 3 new nep wasuken, the position is updated permanently. Otherwiseonly a tempurary estimation for the position iscalculated. The process of position estimation isillustrated i n Fig 5 .

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Figure 5. Location estimatiao using SiLMU and angle mca~u~cmsntF.D. Laser Scan Matching

The localization by using laser range finder iswidely researched in the field of mobile robotics (e.g.[3], [SI and [6]). The results with ZD laser scannershave been very promising and this advocates trying thesame methods for the human localization and mapping.The transfer of algorithms from robots is notstraightfonvard. In mobile robots the sensor is usuallyinstalled statically ou the top of the robot, which is notpossible in the case of a man. The sensor can have verydifferent orientation in the different stages of themovement. This means that the sensor is not measuringin a plane and the fitting of consecutive measurementscan be impossible. Another difference is the lack ofaccurate odometry. Even the robotic odometry issuffering from inaccuracies in the long-term use;between two scans it is usually accurate.

The heading difference is in the common situationsthe most significant source of errors. The waking

causes all the time movement in the heading angle.These changes between headings are usually h m to10 degrees, but the worst case can be up to 40 degrees(when tuming rapidly). The motion between scans isusually 10-30cm in the heading direction. Anotherdifficult error source is the floor (and ceiling)reflections, which occur when the sensor is pointing toolow (or high).

To test the feasibility of using a laser scanner for thehuman localization a simple algorithm was selected.The algorithm is very similar to the one presented in[4], except the matching is done with raw data insteadof evidence grids. The main idea of the algorithm isdescribed below:

1. Generate a set of poses, relative to thereference scan.2. Transform the current scan according to thepose.

3. For every point in the current scan find thenearest neighbor in the reference scan. If theneighbor is closer than a threshold, increasethe bit count.Afler going tbmugh all the poses, select thepose that has the biggest hi t value.4.

For the computing speed reasons the reference scanis sorted according to the x-coordinate (helps to find theclosest neighbor), In many cases the reference scanholds enough information for matching between manysuccessive future scans. The benefit of not changing thereference scan every time is that the m r iven hy thealgorithm is relative to the reference scan. Therefore theerror is not accumulated while scans are matchedagainst a "static" reference scan. The decision forchanging the reference scan is done according to thenumber of pairs found between scans. [Z]

Finally the Least Squares (LSQ) estimation is donefor the best estimate. It is expected that the nearestneighbors in the best estimate are correct and LSQ isused to refine the pose estimate [3].

This algorithm proved to be robust for outliers in thedata, but due to the nature of the algorithm the speed isa problem. Using the initial position estimates ffomS L M U and the heading fiom the Kalman filter thespeed can be increased significantly. Currently thesearch angle is 4 degrees (increment 0.6 degrees) andthe search area is 60x40cm (increment 6cm). Thereason of still having such a wide search area is becauseof improper synchronization of the laser and the gyro,and because the S L M U can give error measurementsffom time to time. Currently only 180 points from alaser scan are used in the scan matching. This reducesthe computation time significantly, but also causessome more drifl to the position estimate.

The matching gives an update to the position, whichis integrated to give the final estimate. The headingresult from the Kalman filter is always maintained, andthus it is not integrated after matching.

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V. TESTS AND RESULTSThe purpose of the tests was to figure out theperformance and the functio~lity f the PeNa systemand ensure that the dead reckoning data is accurateenough for future development like SLAM. The tests

were performed during one-day period and all the testswere reported without selection.The integrated system was tested hy walking aclosed path. Tbis does not give the absolute errorestimate, bu t it gives some approximation of the error.The closed path usually filters out some of the driftsfrom the fmal position, thus he results are s how as fullroute tracks. All the data is processed in real-time whilewaking. The laptop computer has a 1066 MH z PeutiumIl l Mobile CPUand WindowsXP operating system.The test area was the 2nd floor of the TUAS-building in Helsinki University of the Technology. Thewhole test area is illustrated in Fig. 6. The filled area is

the comdor neiwork and the black dot marks are thestartingand ending points of the testms.

-igure 6 . Th e map ofme tcst area

The calculated parameters are:Length of the pathTime ElapsedEnd-Start ErrorRelativeEnorEstimated heading error

The results from the tests are collected in the Table2.Table2 . Th e results of the PeNa tmb.R m c m rw m and C meanscorridor.

distance error was only 0,5m hut due to the headingerror the total error was 2,7m.The short room and corridor test includes one ratherbig laboratory room and a small corridor (Fig. 7). Thetest measures the short-term accuracy in a complicatedenvironment. The result shows that the short-term

accuracy is maintained well.

--.~kt 8.-*-cu-._li-*-----33

Figure 7. ShortCorridor and mom testIn Fig. 8 is illustrated a simple comdor walk (4 inTahle 2). The test result looks nice hut in the end the

heading error has accumulated up to 8 degrees.

.qU L -.to 0 111 yl 4a U (0

F i w . Simple CorridorWalkIn Fig. 9s illustrated a coverage walk (5& inTable 2) that covers all the corridors visible in Fig. 6.The bottom right comdor is actually an open space. Theresult looks very nice, although in the end the heading

error has again influenced quite much to the result.

.

The reference walk was measured similarly as theSiLMU tests. The real length of the path was 110m. The

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. ' .. . . f ..f " "- ~ ...-.?LLk:-,",,';) ,rLZ..-?L...-L-,.22Figure 9. Long conidor walk

The coverage test (Fig. IO ) was done to see thePeNa functionality in large-scale environments. Theduration of the test was over 20min and the length ofthe walked path was almost 60Om. The covered areaincluded all the corridors of the laboratory and all therooms (all which were accessible for the author). Thetotal number of rooms visited was about 40, and aboutin the half of the cases the doors had to he opened. Ingeneral this is a very challenging test for anylocalization system. Fig. IO shows the outward ourney.The rest of the information is left out in order to clarifythe figure. In the end the heading is totally lost.

Figure 10. Th e eovmgc rrplorahan o f Ihc antomtion laboratoryVI. CONCLUSIONS AND F'UTURE WORK

From the very beginning of the PeLoTe project thePeNa demonstrator has been designed to he a dead-reckoning system to support the human localization inindwr conditions. The methods have been transferredmainly from robotics and have been under extensivetesting and evaluation. Currently all the methods havebeen integrated to work in real-time. The performanceis not yet comparable to the state-of-the-art in robotics,but it has to he kept in mind that the problem is muchmore challenging when the localized entity is a humanbeing. Like in robotics the heading estimate is thegreatest source of inaccuracy.

The test results presented in this paper show that thePeNa system provides accurate short-term localizationdata. As in all dead-reckoning systems, drifts affect thelong-term position estimate. The important thing is thatthe PeNa can estimate the position in all kinds of indoorenvironments without loosing the accuracy. This makesthe result feasible to he used with other methods, such

as map-based localization, SLAM and beacon basedlocalization.The heading estimation can and should still beimproved The planned improvement is to fuse all the

information from all the sensors through a positionfilter. Currently the "fusion" is done only through thescan-matching algorithm and in the separate headingestimator.

The goal of the project is to develop a localizationsystem that supports localization of multiple entities inthe same frame of reference. It is expected that somekind of a priori map of the building will he at band Inthe near fu me the aim is to develop and test map basedlocalization for PeNa. The great challenge of the workis to coupe with p d a l a priori map. For t h i s reason alsosome SLAM implementations will be tested The deadreckoning information provides a good base for both ofthese algorithms. Localization is also supported byoperator based position correction. The work will becarried on in the PeLoTe-project, which ends on thespring 2005.

ACKNOWLEDGEMENTSThe work of all authors has been also supportedwithin the ET-2001-FET framework under projectno. 38873 "PeLoTe". The support is gratefullyacknowledged.

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