Robot Localization and Map Building

2010·26.9 MB·English

by  Hannafiah Yussof

I
Robot Localization and Map Building

Robot Localization and Map Building
Edited by
Hanafiah Yussof
In-Tech
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First published March 2010
Printed in India
Technical Editor: Sonja Mujacic
Cover designed by Dino Smrekar
Robot Localization and Map Building,
Edited by Hanafiah Yussof
p. cm.
ISBN 978-953-7619-83-1
V
Preface
Navigation of mobile platform is a broad topic, covering a large spectrum of different
technologies and applications. As one of the important technology highlighting the 21st
century, autonomous navigation technology is currently used in broader spectra, ranging
from basic mobile platform operating in land such as wheeled robots, legged robots,
automated guided vehicles (AGV) and unmanned ground vehicle (UGV), to new application
in underwater and airborne such as underwater robots, autonomous underwater vehicles
(AUV), unmanned maritime vehicle (UMV), flying robots and unmanned aerial vehicle
(UAV).
Localization and mapping are the essence of successful navigation in mobile platform
technology. Localization is a fundamental task in order to achieve high levels of autonomy
in robot navigation and robustness in vehicle positioning. Robot localization and mapping is
commonly related to cartography, combining science, technique and computation to build
a trajectory map that reality can be modelled in ways that communicate spatial information
effectively. The goal is for an autonomous robot to be able to construct (or use) a map or floor
plan and to localize itself in it. This technology enables robot platform to analyze its motion
and build some kind of map so that the robot locomotion is traceable for humans and to ease
future motion trajectory generation in the robot control system. At present, we have robust
methods for self-localization and mapping within environments that are static, structured,
and of limited size. Localization and mapping within unstructured, dynamic, or large-scale
environments remain largely an open research problem.
Localization and mapping in outdoor and indoor environments are challenging tasks in
autonomous navigation technology. The famous Global Positioning System (GPS) based
on satellite technology may be the best choice for localization and mapping at outdoor
environment. Since this technology is not applicable for indoor environment, the problem
of indoor navigation is rather complex. Nevertheless, the introduction of Simultaneous
Localization and Mapping (SLAM) technique has become the key enabling technology for
mobile robot navigation at indoor environment. SLAM addresses the problem of acquiring a
spatial map of a mobile robot environment while simultaneously localizing the robot relative
to this model. The solution method for SLAM problem, which are mainly introduced in
this book, is consists of three basic SLAM methods. The first is known as extended Kalman
filters (EKF) SLAM. The second is using sparse nonlinear optimization methods that based
on graphical representation. The final method is using nonparametric statistical filtering
techniques known as particle filters. Nowadays, the application of SLAM has been expended
to outdoor environment, for use in outdoor’s robots and autonomous vehicles and aircrafts.
Several interesting works related to this issue are presented in this book. The recent rapid
VI
progress in sensors and fusion technology has also benefits the mobile platforms performing
navigation in term of improving environment recognition quality and mapping accuracy. As
one of important element in robot localization and map building, this book presents interesting
reports related to sensing fusion and network for optimizing environment recognition in
autonomous navigation.
This book describes comprehensive introduction, theories and applications related to
localization, positioning and map building in mobile robot and autonomous vehicle platforms.
It is organized in twenty seven chapters. Each chapter is rich with different degrees of details
and approaches, supported by unique and actual resources that make it possible for readers
to explore and learn the up to date knowledge in robot navigation technology. Understanding
the theory and principles described in this book requires a multidisciplinary background of
robotics, nonlinear system, sensor network, network engineering, computer science, physics,
etc.
The book at first explores SLAM problems through extended Kalman filters, sparse nonlinear
graphical representation and particle filters methods. Next, fundamental theory of motion
planning and map building are presented to provide useful platform for applying SLAM
methods in real mobile systems. It is then followed by the application of high-end sensor
network and fusion technology that gives useful inputs for realizing autonomous navigation
in both indoor and outdoor environments. Finally, some interesting results of map building
and tracking can be found in 2D, 2.5D and 3D models. The actual motion of robots and
vehicles when the proposed localization and positioning methods are deployed to the system
can also be observed together with tracking maps and trajectory analysis. Since SLAM
techniques mostly deal with static environments, this book provides good reference for future
understanding the interaction of moving and non-moving objects in SLAM that still remain as
open research issue in autonomous navigation technology.
Hanafiah Yussof
Nagoya University, Japan
Universiti Teknologi MARA, Malaysia
VII
Contents
Preface V
1. Visual Localisation of quadruped walking robots 001
Renato Samperio and Huosheng Hu
2. Ranging fusion for accurating state of the art robot localization 027
Hamed Bastani and Hamid Mirmohammad-Sadeghi
3. Basic Extended Kalman Filter – Simultaneous Localisation and Mapping 039
Oduetse Matsebe, Molaletsa Namoshe and Nkgatho Tlale
4. Model based Kalman Filter Mobile Robot Self-Localization 059
Edouard Ivanjko, Andreja Kitanov and Ivan Petrović
5. Global Localization based on a Rejection Differential Evolution Filter 091
M.L. Muñoz, L. Moreno, D. Blanco and S. Garrido
6. Reliable Localization Systems including GNSS Bias Correction 119
Pierre Delmas, Christophe Debain, Roland Chapuis and Cédric Tessier
7. Evaluation of aligning methods for landmark-based maps in visual SLAM 133
Mónica Ballesta, Óscar Reinoso, Arturo Gil, Luis Payá and Miguel Juliá
8. Key Elements for Motion Planning Algorithms 151
Antonio Benitez, Ignacio Huitzil, Daniel Vallejo, Jorge de la Calleja and Ma. Auxilio Medina
9. Optimum Biped Trajectory Planning for Humanoid Robot Navigation in Unseen
Environment 175
Hanafiah Yussof and Masahiro Ohka
10. Multi-Robot Cooperative Sensing and Localization 207
Kai-Tai Song, Chi-Yi Tsai and Cheng-Hsien Chiu Huang
11. Filtering Algorithm for Reliable Localization of Mobile Robot in Multi-Sensor
Environment 227
Yong-Shik Kim, Jae Hoon Lee, Bong Keun Kim, Hyun Min Do and Akihisa Ohya
12. Consistent Map Building Based on Sensor Fusion for Indoor Service Robot 239
Ren C. Luo and Chun C. Lai
VIII
13. Mobile Robot Localization and Map Building for a Nonholonomic Mobile Robot 253
Songmin Jia and AkiraYasuda
14. Robust Global Urban Localization Based on Road Maps 267
Jose Guivant, Mark Whitty and Alicia Robledo
15. Object Localization using Stereo Vision 285
Sai Krishna Vuppala
16. Vision based Systems for Localization in Service Robots 309
Paulraj M.P. and Hema C.R.
17. Floor texture visual servo using multiple cameras for mobile robot localization 323
Takeshi Matsumoto, David Powers and Nasser Asgari
18. Omni-directional vision sensor for mobile robot navigation based on particle filter 349
Zuoliang Cao, Yanbin Li and Shenghua Ye
19. Visual Odometry and mapping for underwater Autonomous Vehicles 365
Silvia Botelho, Gabriel Oliveira, Paulo Drews, Mônica Figueiredo and Celina Haffele
20. A Daisy-Chaining Visual Servoing Approach with Applications in
Tracking, Localization, and Mapping 383
S. S. Mehta, W. E. Dixon, G. Hu and N. Gans
21. Visual Based Localization of a Legged Robot with a topological representation 409
Francisco Martín, Vicente Matellán, José María Cañas and Carlos Agüero
22. Mobile Robot Positioning Based on ZigBee Wireless Sensor
Networks and Vision Sensor 423
Wang Hongbo
23. A WSNs-based Approach and System for Mobile Robot Navigation 445
Huawei Liang, Tao Mei and Max Q.-H. Meng
24. Real-Time Wireless Location and Tracking System with Motion Pattern Detection 467
Pedro Abreua, Vasco Vinhasa, Pedro Mendesa, Luís Paulo Reisa and Júlio Gargantab
25. Sound Localization for Robot Navigation 493
Jie Huang
26. Objects Localization and Differentiation Using Ultrasonic Sensors 521
Bogdan Kreczmer
27. Heading Measurements for Indoor Mobile Robots with Minimized
Drift using a MEMS Gyroscopes 545
Sung Kyung Hong and Young-sun Ryuh
28. Methods for Wheel Slip and Sinkage Estimation in Mobile Robots 561
Giulio Reina
Visual Localisation of quadruped walking robots 1
Visual Localisation of quadruped walking robots
01
Visual Localisation of quadruped walking robots
Renato Samperio and Huosheng Hu
RenatoSamperioandHuoshengHu
SchoolofComputerScienceandElectronicEngineering,UniversityofEssex
UnitedKingdom
1. Introduction
Recently, several solutions to the robot localisation problem have been proposed in the sci-
entific community. In this chapter we present a localisation of a visual guided quadruped
walkingrobotinadynamicenvironment.Weinvestigatethequalityofrobotlocalisationand
landmarkdetection,inwhichrobotsperformtheRoboCupcompetition(Kitanoetal.,1997).
Thefirstpartpresentsanalgorithmtodetermineanyentityofinterestinaglobalreference
frame,wheretherobotneedstolocatelandmarkswithinitssurroundings.Inthesecondpart,
afastandhybridlocalisationmethodisdeployedtoexplorethecharacteristicsoftheproposed
localisationmethodsuchasprocessingtime,convergenceandaccuracy.
Ingeneral,visuallocalisationofleggedrobotscanbeachievedbyusingartificialandnatural
landmarks. Thelandmarkmodellingproblemhasbeenalreadyinvestigatedbyusingprede-
finedlandmarkmatchingandreal-timelandmarklearningstrategiesasin(Samperio&Hu,
2010). Also,byfollowingthepre-attentiveandattentivestagesofpreviouslypresentedwork
of(Quocetal.,2004),weproposealandmarkmodelfordescribingtheenvironmentwith"in-
teresting"featuresasin(Lukeetal.,2005),andtomeasurethequalityoflandmarkdescription
andselectionovertimeasshownin(Watmanetal.,2004). Specifically,weimplementvisual
detectionandmatchingphasesofapre-definedlandmarkmodelasin(Hayetetal.,2002)and
(Sungetal.,1999),andforreal-timerecognisedlandmarksinthefrequencydomain(Maosen
etal.,2005)wheretheyareaddressedbyasimilarityevaluationprocesspresentedin(Yoon
&Kweon,2001). Furthermore,wehaveevaluatedtheperformanceofproposedlocalisation
methods,Fuzzy-Markov(FM),ExtendedKalmanFilters(EKF)andancombinedsolutionof
Fuzzy-Markov-Kalman(FM-EKF),asin(Samperioetal.,2007)(Haticeetal.,2006).
Theproposedhybridmethodintegratesaprobabilisticmulti-hypothesisandgrid-basedmaps
withEKF-basedtechniques. Asitispresentedin(Kristensen&Jensfelt,2003)and(Gutmann
etal.,1998),somemethodologiesrequireanextensivecomputationbutofferareliableposi-
tioning system. By cooperating a Markov-based method into the localisation process (Gut-
mann,2002),EKFpositioningcanconvergefasterwithaninaccurategridobservation. Also.
Markov-based techniques and grid-based maps (Fox et al., 1998) are classic approaches to
robotlocalisationbuttheircomputationalcostishugewhenthegridsizeinamapissmall
(Duckett & Nehmzow, 2000) and (Jensfelt et al., 2000) for a high resolution solution. Even
theproblemhasbeenpartiallysolvedbytheMonteCarlo(MCL)technique(Foxetal.,1999),
(Thrun et al., 2000) and (Thrun et al., 2001), it still has difficulties handling environmental
changes(Tanakaetal.,2004). Ingeneral,EKFmaintainsacontinuousestimationofrobotpo-
sition,butcannotmanagemulti-hypothesisestimationsasin(Baltzakis&Trahanias,2002).
2 Robot Localization and Map Building
Moreover,traditionalEKFlocalisationtechniquesarecomputationallyefficient,buttheymay
alsofailwithquadrupedwalkingrobotspresentpoorodometry,insituationssuchaslegslip-
pageandlossofbalance.Furthermore,weproposeahybridlocalisationmethodtoeliminate
inconsistencies and fuse inaccurate odometry data with costless visual data. The proposed
FM-EKF localisation algorithm makes use of a fuzzy cell to speed up convergence and to
maintainanefficientlocalisation.Subsequently,theperformanceoftheproposedmethodwas
testedinthreeexperimentalcomparisons:(i)simplemovementsalongthepitch,(ii)localising
andplayingcombinedbehavioursandc)kidnappingtherobot.
Therestofthechapterisorganisedasfollows. FollowingthebriefintroductionofSection1,
Section2describestheproposedobservermoduleasanupdatingprocessofaBayesianlo-
calisationmethod.Also,robotmotionandmeasurementmodelsarepresentedinthissection
forreal-time landmarkdetection. Section 3investigatesthe proposedlocalisationmethods.
Section4presentsthesystemarchitecture.Someexperimentalresultsonlandmarkmodelling
andlocalisationarepresentedinSection5toshowthefeasibilityandperformanceofthepro-
posedlocalisationmethods.Finally,abriefconclusionisgiveninSection6.
2. Observerdesign Fig.1.TheproposedmotionmodelforAibowalkingrobot
Thissectiondescribesarobotobservermodelforprocessingmotionandmeasurementphases.
Thesephases,alsoknownasPredictandUpdate,involveastateestimationinatimesequence Accordingtotheempiricalexperimentaldata, theodometrysystempresentsadeviationof
forrobotlocalisation.Additionally,ateachphasethestateisupdatedbysensinginformation 30%onaverageasshowninEquation(4.12). Therefore,byapplyingatransformationmatrix
andmodellingnoiseforeachprojectedstate. WtfromEquation(4.13),noisecanbeaddressedasrobotuncertaintywhereθpointstherobot
heading.
2.1 MotionModel
Thestate-spaceprocessrequiresastatevectorasprocessingandpositioningunitsinanob- (0.3ulin)2 0 0
t
serverdesignproblem.Thestatevectorcontainsthreevariablesforrobotlocalisation,i.e.,2D Qt = 0 (0.3ultat)2 0  (4.12)
position(x,y)andorientation(θ). Additionally,thepredictionphaseincorporatesnoisefrom 0 0 (0.3urot+ √(ultin)2+(ultat)2)2
robotodometry,asitispresentedbelow:  t 500 
 
cosθ senθ 0
 yxθt−−tt− = yxθttt−−−111 + ((uultltiinn++wwttlliinn))csionsθθuttrt−−o1t1++−w((uurtoltltaattt++wwtltlaatt))csoinsθθtt−−11  (4.9) Wt = fw= sen0θtt−−11 −cos0θt−t−11 01  (4.13)
where ulat, ulin and urot are the lateral, linear and rotational components of odometry, and 2.2 MeasurementModel
wlat, wlin and wrot are the lateral, linear and rotational components in errors of odometry. Inordertorelatetherobottoitssurroundings,wemakeuseofalandmarkrepresentation.The
landmarksintherobotenvironmentrequirenotationalrepresentationofameasuredvector fi
Also,t 1referstothetimeoftheprevioustimestepandttothetimeofthecurrentstep. t
− foreachi-thfeatureasitisdescribedinthefollowingequation:
Ingeneral, stateestimationisaweightedcombinationofnoisystatesusingbothprioriand
posteriorestimations. Likewise,assumingthatvisthemeasurementnoiseandwisthepro-
r1 r2
cessnoise,theexpectedvalueofthemeasurementRandprocessnoiseQcovariancematrixes t t
areexpressedseparatelyasinthefollowingequations: f(zt)={ft1,ft2,...}={ bt1 , bt2 ,...} (4.14)
s1 s2
t t
R=E[vvt] (4.10)    
wherelandmarksaredetectedbyanonboardactivecameraintermsofrangeri, bearing bi
t t
Q=E[wwt] (4.11) andasignaturesi foridentifyingeachlandmark.Alandmarkmeasurementmodelisdefined
t
Themeasurementnoiseinmatrix RrepresentssensorerrorsandtheQmatrixisalsoacon- byafeature-basedmapm, whichconsistsofalistofsignaturesandcoordinatelocationsas
fidence indicator for current prediction which increases or decreases state uncertainty. An follows:
odometrymotionmodel,u isadoptedasshowninFigure1. Moreover,Table1describes
t 1
allvariablesforthreedimen−sional(linear,lateralandrotational)odometryinformationwhere m={m1,m2,...}={(m1,x,m1,y),(m2,x,m2,y),...} (4.15)
(x,y)istheestimatedvaluesand(x,y)theactualstates.
(cid:27) (cid:27)