Terrain Aided Localisation of Autonomous Vehicles in Unstructured Environments

by Rajmohan Madhavan

ISBN: 1-58112-177-6

DISSERTATION.COM

Parkland, FL • USA • 2003

Terrain Aided Localisation of Autonomous Vehicles in Unstructured Environments

Copyright © 2001 Rajmohan Madhavan All rights reserved.

Dissertation.com USA • 2003

ISBN: 1-58112-177-6

www.Dissertation.com/library/1121776a.htm

Terrain Aided Localisation ofAutonomous Vehicles in

Unstructured Environments

Rajmohan Madhavan

A Thesis submitted in fulfillmentof the requirements for the degree of

Doctor of Philosophy

SID

ERE ·MEN

S ·EADEM

·MUTAT

O

Australian Centre for Field RoboticsDepartment of Mechanical and Mechatronic Engineering

The University of Sydney

January 2001

To my parents, my lovely wife Kalpana and our beloved daughter Priyanka.

Foreword

About the Author:

Rajmohan Madhavan was born in Madras, India, in 1973. He received the Bachelor ofEngineering degree (Electrical and Electronics) in 1995 from the College of Engineering,Guindy, Anna University, India and the Master of Engineering (Research) degree (Controland Robotics) in 1997 from the Department of Engineering, The Australian National Uni-versity, Australia. He received his Ph.D. degree (Field Robotics) in 2001 from the Schoolof Aerospace, Mechanical and Mechatronic Engineering (Department of Mechanical andMechatronic Engineering at the time of completion), The University of Sydney, Sydney,Australia. Since 2001, he has been a research associate with the Oak Ridge National Lab-oratory (ORNL) and is currently a guest researcher with the Intelligent Systems Divisionof the National Institute of Standards and Technology (NIST). His current research inter-ests include autonomous vehicle navigation in large and complex environments, distributedheterogenous sensing and systems and control theory.

Author’s Contact Email: raj_madhavan@yahoo.com

Declaration

I hereby declare that this submission is my own work and that, to the best of my knowl-edge and belief, it contains no material previously published or written by another personnor material which to a substantial extent has been accepted for the award of any otherdegree or diploma of the University or other Institute of higher learning, except where dueacknowledgement has been made in the text.

Rajmohan MadhavanJanuary 08 2001.

c©2001 Rajmohan MadhavanAll Rights Reserved

AbstractThis thesis is concerned with the theoretical and practical development of reliable androbust localisation algorithms for autonomous land vehicles operating at high speeds in un-structured, expansive and harsh environments. Localisation is the ability of a vehicle todetermine its position and orientation within an operating environment. The need for sucha localisation system is motivated by the requirement of developing autonomous vehiclesin applications such as mining, agriculture and cargo handling. The main drivers in theseapplications are for safety, efficiency and productivity. The approach taken to the localisa-tion problem in this thesis guarantees that the safety and reliability requirements imposedby such applications are achieved. The approach also aims to minimise the engineering ormodification of the environment, such as adding artificial landmarks or other infrastructure.This is a key driver in the practical implementation of a localisation algorithm.

In pursuit of these objectives, this thesis makes the following principal contributions:

1. The development of an Iterative Closest Point - Extended Kalman Filter (ICP-EKF)algorithm - a map-based iconic algorithm that utilises measurements from a scanninglaser rangefinder to achieve localisation. The ICP-EKF algorithm entails the develop-ment of a map-building algorithm. The main attraction of the map-based localisationalgorithm is that it works directly on sensed data and thus does not require extractionand matching of features. It also explicitly takes into account the uncertainty associ-ated with measurements and has the ability to include measurements from a varietyof different sensors.

2. The development and implementation of an entropy-based metric to evaluate the in-formation content of measurements. This metric facilitates the augmentation of land-marks to the ICP-EKF algorithm thus guaranteeing reliable and robust localisation.

3. The development and adaptation of a view-invariant Curvature Scale Space (CSS)landmark extraction algorithm. The algorithm is sufficiently robust to sensor noiseand is capable of reliably detecting and extracting landmarks that are naturally presentin the environment from laser rangefinder scans.

4. The integration of the information metric and the CSS and ICP-EKF algorithms toarrive at a unified localisation framework that uses measurements from both artifi-cial and natural landmarks, combined with dead-reckoning sensors, to deliver reliablevehicle position estimates.

The localisation framework developed is sufficiently generic to be used on a variety of otherautonomous land vehicle systems. This is demonstrated by its implementation using fielddata collected from three different trials on three different vehicles. The first trial wascarried out on a four-wheel drive vehicle in an underground mine tunnel. The second trialwas conducted on a Load-Haul-Dump (LHD) truck in a test tunnel constructed to emulatean underground mine. The estimates of the proposed localisation algorithms are comparedto the ground truth provided by an artificial landmark-based localisation algorithm that usesbearing measurements from a laser. To demonstrate the feasibility and reliability of boththe natural landmark extraction and localisation algorithms, these are also implemented ona utility vehicle in an outdoor area within the University’s campus. The results demonstratethe robustness of the proposed localisation algorithms in producing reliable and accurateposition estimates for autonomous vehicles operating in a variety of unstructured domains.

Acknowledgements

“The human mind is not capable of grasping the Universe. We are like a little child entering a

huge library. The walls are covered to the ceilings with books in many different tongues. The

child knows that someone must have written these books. It does not know who or how. It does

not understand the languages in which they are written. But the child notes a definite plan in the

arrangement of the books - a mysterious order which it does not comprehend, but only dimly suspects”

- Albert Einstein

Let me start by thanking Dr. Gamini Dissanayake and Prof. Hugh Durrant-Whyte for theirsupport and guidance throughout the Ph.D. program. Dissa taught me to think criticallyand to always keep the bigger picture in mind. I appreciate all his help and suggestionsduring my candidature. My sincere thanks are due to Hugh for giving me an opportunity towork with the ACFR and for his enthusiasm and direction. I found both of them to be veryapproachable and willing to assist in every step of the way. The years I have spent with theACFR and the CMTE have been extremely rewarding and has been a time of personal andprofessional growth. Most of all, it’s been a humbling experience.

The three field trials that are reported in this thesis would not have been possible but forthe following people:

Eric Nettleton for the 4WD trials - Eric’s help with data-logging and calibration trialsis very much appreciated. Sorry about the freezing conditions in Forest Lodge in Brisbane!

The CSIRO/CMTE/AMIRA team for the LHD trials - I would like to thank the team andespecially Jonathan Roberts. Jon was always willing to help every time. I am much obligedfor his help. I would also like to thank the CSIRO team for their help in various issuesduring several visits to QCAT with Eric Nettleton and Ali Goktogan: Jock Cunningham,Graeme Winstanley, Stuart Wolfe, Leslie Overs, Reece McCasker, Elliot Duff, Peter Corkeand Pavan Sikka.

Jose "Se~nor" Guivant for the Utility vehicle trials - Jose’s help and time in gath-ering the field data is also much appreciated. He’s been a good friend and long will I re-member our conversations way past midnight on subjects ranging from the frivolous mateto the very existence of life. Gracias, mi amigo.

Thanks also goes to CMTE for its financial assistance in the form of a PhD studentship.CMTE’s financial support to attend the SME Annual meeting to receive the outstandingstudent paper award is also much appreciated. I would like to thank CEO Mike Hood, Ed-ucation Program Leader Dominic Howarth and Hal Gurgenci. ACFR’s financial assistancefor several conferences and visits is also gratefully acknowledged.

vi

I would also like to thank:

• ACFR and the University of Sydney gang: Anna McCallan for helping me to sort outadministrative issues especially in the last year among other things, Eduardo Nebotfor advice on career plans, Som Majumder and Stefan Williams for being my cubicle-mates and for putting up with my self-cursing and incessant questions, Quang Nguyenand Miguel Santos for those intense ping-pong games, Mike Stevens for his systemadministrative help (who also does an almost perfect Indian accent), Ralph Koch forhis help with my temperamental bike, Ben Grocholsky for discussions on entropy, TimBailey for discussions on Blues and in general on guitarists, Steve Scheding for his helpduring a visit to CMU, Salah Sukkarieh for all those wonderful Lebanese sweets, Trevor“Irishman” Fitzgibbons, Keith Willis, Tomonari Furukawa, Monica Louda, Jong-HyukKim, Chris Misfud, Ikramy Garas, Ali Goktogan for a trip to Montville and the turkishdelight, Gurce Isikyildiz for introducing me to the Saz, Maher “Sugar” Magrabi, Jeff“Hotpants” Leal for introducing me to the Dave Matthews Band, Julio Rosenblatt,Vinita Martin, Lyn Kennedy and Susan Gonzalez.

• Brisbane gang: Adrian and Sanda Bonchis, Bradley and Natalie Horton, JasmineBanks, Yvonne Kolatschek, Shivakumar Karekal, Tricia Dolphin, Helene Marszalekand Lena Mete for their assistance and company during several visits to CMTE.

• Visitors to ACFR: Henrik “Ribs” Svedlund, Joachim “Sir Jake” Andersson, SvenRonnback and Tobias Carlsson (from Sweden), Gerold Kloos and Stefan Baiker (fromGermany), Rob Dawkins (from UK), Guillame Lebas (from France) for introducingme to the cultures and customs of their countries and teaching me a little bit of theirlanguages (especially the words that I could have done without during visits to theircountries!). These guys helped me to go and see places in Sydney and elsewhere thatI, as a Sydneysider, for the best part of the last three years, never knew of theirexistence! Thanks fellas.

• My friends Rittwik Jana, Chandar Muthukrishnan, Rajesh Sambandam, Magesh Govin-darajan, Venkatesh Babu, Rajkumar Kandasamy, Kangesh Gunaseelan, Devaraj Na-garajan and Sharon Laubach - These guys kept me sane during the later half of thePh.D. (even though most of them are on the other side of the Pacific!), Simon Julierfor discussions with the Unscented filter and life otherwise.

• Everyone else whom I have forgotten to mention.

I reserve the greatest thanks to my parents and to my brother. They have stood by me allthese years and I am indebted for their love, support and prayers. At the end of it all, I canalways go home.

Nomenclature

Only frequently used notations are included here.

xv x coordinate of the vehicleyv y coordinate of the vehicleφv orientation of the vehiclerv wheel radius of the vehicleαv rear slip angle of the LHDβv front slip angle of the LHDssv shaping state for the gyro drift

R range of the scanning laser rangefinderθ bearing of the scanning laser rangefinderθgcs bearing of the bearing-only laserRj

n� range of the jth natural landmarkθj

n� bearing of the jth natural landmark(Xi, Yi) cartesian location of artificial landmark i

(xjn�, y

jn�) cartesian location of natural landmark j

xk vehicle state at discrete time instant kuk vehicle control input at discrete time instant kzk vehicle observations (measurements) available

at discrete time instant kx(k|k−1) predicted vehicle state at discrete time instant k

based on the state estimate at time instant (k − 1)P(k|k−1) predicted vehicle covariance at discrete time instant k

based on the covariance estimate at time instant (k − 1)x(k|k) updated vehicle state at discrete time instant k obtained

by combining the predicted estimate at time instant kand the observation(s) at time instant k

P(k|k) updated vehicle covariance at discrete time instant k obtainedby combining the predicted covariance estimate at time instant kand the observation(s) at time instant k

E [·] mathematical expectation operator

Abbreviations

ACFR Australian Centre for Field RoboticsAMIRA Australian Mineral Industries Research AssociationCSIRO Commonwealth Scientific Industrial Research OrganisationCRC Cooperative Research CentreCMTE CRC for Mining Technology and EquipmentCMST CSIRO Manufacturing Science and TechnologyE & M CSIRO Exploration and Mining

EKF Extended Kalman FilterMMSE Minimum Mean Squared EstimateICP Iterative Closest PointCSS Curvature Scale Space4WD Four-Wheel DriveLHD Load-Haul-DumpAGV Automated Guided Vehicle

TOF Time Of FlightGPS Global Positioning SystemIMU Inertial Measurement UnitINS Inertial Navigation System

Contents

Foreword ii

Declaration iii

Abstract iv

Acknowledgements v

Nomenclature vii

Abbreviations viii

Contents ix

List ofFigures xiii

List of Tables xviii

1 Introduction 1

1.1 The Aims and Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.2 Motivation and Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

1.2.1 Sensor-based Localisation . . . . . . . . . . . . . . . . . . . . . . . . . 2

1.3 Contributions of the Thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

1.4 The Structure of the Thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

2 Artificial Landmark-based Localisation 8

2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

2.2 An Overview of Landmark-based Navigation . . . . . . . . . . . . . . . . . . 9

2.3 An Overview of Underground Vehicle Automation . . . . . . . . . . . . . . . 12

2.4 The Four-wheel Drive Trial Vehicle and the Sensor Suite . . . . . . . . . . . . 15

x Contents

2.4.1 Bearing-only Laser . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

2.4.2 Inertial Measurement Unit . . . . . . . . . . . . . . . . . . . . . . . . 18

2.4.3 Range and Bearing Laser . . . . . . . . . . . . . . . . . . . . . . . . . 18

2.4.4 Global Positioning System . . . . . . . . . . . . . . . . . . . . . . . . . 19

2.4.5 Encoders . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

2.5 The Data Logging System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

2.5.1 Hardware . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

2.5.2 Sensor Specifications for the Four-wheel Drive Trials . . . . . . . . . . 22

2.5.3 Software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

2.6 Calibration Trials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

2.6.1 Steering Encoder Calibration . . . . . . . . . . . . . . . . . . . . . . . 24

2.6.2 Shaft Encoder Calibration . . . . . . . . . . . . . . . . . . . . . . . . . 24

2.6.3 Wheel Encoder Calibration . . . . . . . . . . . . . . . . . . . . . . . . 26

2.7 Artificial Landmark-based Localisation of the 4WD Vehicle . . . . . . . . . . 26

2.7.1 Process Model for the 4WD Vehicle . . . . . . . . . . . . . . . . . . . 26

2.7.2 Observation Model for the Bearing-only laser . . . . . . . . . . . . . . 30

2.7.3 Observation Model for the Gyro . . . . . . . . . . . . . . . . . . . . . 30

2.7.4 The Estimation Cycle . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

2.7.5 4WD Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . 32

2.8 Artificial Landmark-based Localisation of the LHD . . . . . . . . . . . . . . . 43

2.8.1 Experimental Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

2.8.2 Process Model for the LHD . . . . . . . . . . . . . . . . . . . . . . . . 44

2.8.3 Observation Model for the Bearing-only Laser . . . . . . . . . . . . . . 46

2.8.4 Observation Model for the Gyro . . . . . . . . . . . . . . . . . . . . . 46

2.8.5 The Estimation Cycle . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

2.8.6 LHD Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . 49

2.9 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62

3 Map-based Iconic Localisation 63

3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

3.2 An Overview of Map-based Navigation . . . . . . . . . . . . . . . . . . . . . . 64

3.3 Map Building . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68

3.4 Map-based Localisation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71

3.4.1 Process Model for the ICP-EKF Algorithm . . . . . . . . . . . . . . . 71

xi

3.4.2 Observation Model for the Gyro . . . . . . . . . . . . . . . . . . . . . 72

3.4.3 Observation Model for the ICP-EKF Algorithm . . . . . . . . . . . . . 72

3.4.4 The ICP-EKF Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . 74

3.5 Results and Discussion for the 4WD Vehicle . . . . . . . . . . . . . . . . . . . 81

3.6 Shortcomings of the ICP-EKF Algorithm . . . . . . . . . . . . . . . . . . . . 94

3.7 Entropy-based Landmark Augmentation . . . . . . . . . . . . . . . . . . . . . 94

3.7.1 The Concept of Entropy . . . . . . . . . . . . . . . . . . . . . . . . . . 95

3.7.2 Landmark Selection and Augmentation . . . . . . . . . . . . . . . . . 97

3.8 Results and Discussion for the LHD . . . . . . . . . . . . . . . . . . . . . . . 102

3.9 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114

4 Multiscale Natural Landmark Extraction 115

4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115

4.2 Multiscale Shape Description . . . . . . . . . . . . . . . . . . . . . . . . . . . 116

4.2.1 The Concept of Scale . . . . . . . . . . . . . . . . . . . . . . . . . . . 117

4.2.2 Scale Space Filtering . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118

4.2.3 The Attraction and Appeal of Scale Space . . . . . . . . . . . . . . . . 119

4.2.4 Uniqueness of the Gaussian Kernel for Scale Space Filtering . . . . . . 120

4.3 Natural Landmark Extraction via Curvature Scale Space . . . . . . . . . . . . 121

4.3.1 Theory of Curvature Scale Space . . . . . . . . . . . . . . . . . . . . . 121

4.3.2 Laser Scan Segmentation . . . . . . . . . . . . . . . . . . . . . . . . . 124

4.3.3 Errors with Sub-sampling . . . . . . . . . . . . . . . . . . . . . . . . . 124

4.3.4 Path Length Parametrisation . . . . . . . . . . . . . . . . . . . . . . . 127

4.3.5 Scale Space Map Construction . . . . . . . . . . . . . . . . . . . . . . 130

4.4 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131

4.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144

5 Landmark Augmented Minimal Infrastructure Localisation 145

5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145

5.2 Minimal Landmark Augmented ICP-EKF Localisation . . . . . . . . . . . . . 145

5.2.1 Observation Model for Natural Landmarks . . . . . . . . . . . . . . . 146

5.2.2 Entropy-based Artificial Landmark Selection andAugmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146

5.2.3 Natural Landmark Matching and Discriminance . . . . . . . . . . . . 147

5.2.4 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . 149

xii Contents

5.3 Natural Landmark-based Localisation of a Utility Vehicle . . . . . . . . . . . 162

5.3.1 The Trial Vehicle and Experimental Setup . . . . . . . . . . . . . . . . 162

5.3.2 Process Model for the Utility Vehicle . . . . . . . . . . . . . . . . . . . 163

5.3.3 Natural Landmark Mapping and Localisation . . . . . . . . . . . . . . 166

5.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174

6 Conclusions and Further Research 175

6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 175

6.2 Summary and Contributions of the Thesis . . . . . . . . . . . . . . . . . . . . 175

6.3 Directions for Future Research . . . . . . . . . . . . . . . . . . . . . . . . . . 179

6.3.1 High Integrity Navigation . . . . . . . . . . . . . . . . . . . . . . . . . 179

6.3.2 Global Localisation and Non-Gaussian Distributions . . . . . . . . . . 179

6.3.3 Simultaneous Localisation and Map-building . . . . . . . . . . . . . . 180

6.3.4 Terrain Uncertainty via Entropy . . . . . . . . . . . . . . . . . . . . . 180

6.3.5 Extension to Other Field Robotics Environments . . . . . . . . . . . . 181

A The Kalman Filter in Navigation 182

A.1 The Linear Kalman Filter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 182

A.2 The Extended Kalman Filter . . . . . . . . . . . . . . . . . . . . . . . . . . . 186

Bibliography 189

xiii

List of Figures

2.1 Snapshots of LHDs in various mine tunnels. . . . . . . . . . . . . . . . . . . . 13

2.2 The 4WD trial vehicle and its sensor suite. . . . . . . . . . . . . . . . . . . . 16

2.3 The bearing-only laser. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

2.4 The scanning laser rangefinder. . . . . . . . . . . . . . . . . . . . . . . . . . . 19

2.5 Encoder mounting arrangements on the 4WD vehicle. . . . . . . . . . . . . . 21

2.6 Data logging setup. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

2.7 Encoder interface and transputer housing. . . . . . . . . . . . . . . . . . . . . 25

2.8 Sensor suite outputs after processing. . . . . . . . . . . . . . . . . . . . . . . . 27

2.9 Process and observation model geometries. . . . . . . . . . . . . . . . . . . . . 28

2.10 Shaft and steering encoder based dead-reckoning paths and pose errors. . . . 33

2.11 Vehicle speed and steering angle for 4WD trial #1. . . . . . . . . . . . . . . . 34

2.12 Vehicle path and orientation estimated by the artificial landmark-based algo-rithm for 4WD trial #1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

2.13 The estimated standard deviation of the vehicle pose and the laser innovationsequence with the 2σ confidence bounds obtained by the artificial landmark-based algorithm for 4WD trial #1. . . . . . . . . . . . . . . . . . . . . . . . . 36

2.14 Wheel radius and its standard deviation obtained by the artificial landmark-based algorithm for 4WD trial #1. . . . . . . . . . . . . . . . . . . . . . . . . 37

2.15 Vehicle speed and steering angle for 4WD trial #2. . . . . . . . . . . . . . . . 38

2.16 Vehicle path and orientation estimated by the artificial landmark-based algo-rithm for 4WD trial #2. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

2.17 The estimated standard deviation of the vehicle pose and the laser innovationsequence with the 2σ confidence bounds obtained by the artificial landmark-based algorithm for 4WD trial #2. . . . . . . . . . . . . . . . . . . . . . . . . 41

2.18 Wheel radius and its standard deviation obtained by the artificial landmark-based algorithm for 4WD trial #2. . . . . . . . . . . . . . . . . . . . . . . . . 42

2.19 The Load-Haul-Dump truck and its sensor suite. . . . . . . . . . . . . . . . . 43

2.20 Kinematic representation of the LHD. . . . . . . . . . . . . . . . . . . . . . . 44

2.21 Vehicle speed and rate of change of orientation for LHD trial #1. . . . . . . . 50

xiv List of figures

2.22 Articulation angle and its computed derivative for LHD trial #1. . . . . . . . 51

2.23 Vehicle path and orientation estimated by the artificial landmark-based algo-rithm for LHD trial #1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

2.24 The estimated standard deviation of the vehicle pose and the laser innovationsequence with the 2σ confidence bounds obtained by the artificial landmark-based algorithm for LHD trial #1. . . . . . . . . . . . . . . . . . . . . . . . . 53

2.25 The estimated wheel radius, the rear slip angle and their standard deviationsfor LHD trial #1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

2.26 The estimated front slip angle, the shaping state and their standard deviationsfor LHD trial #1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

2.27 Vehicle speed and rate of change of orientation for LHD trial #2. . . . . . . . 56

2.28 Articulation angle and its computed derivative for LHD trial #2. . . . . . . . 56

2.29 Vehicle path and orientation estimated by the artificial landmark-based algo-rithm for LHD trial #2. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58

2.30 The estimated standard deviation of the vehicle pose and the laser innovationsequence with the 2σ confidence bounds obtained by the artificial landmark-based algorithm for LHD trial #2. . . . . . . . . . . . . . . . . . . . . . . . . 59

2.31 The estimated wheel radius, the rear slip angle and their standard deviationsfor LHD trial #2. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

2.32 The estimated front slip angle, the shaping state and their standard deviationsfor LHD trial #2. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61

3.1 2D range and bearing laser scan in a straight section of the underground tunnel. 69

3.2 Length of the perpendicular. . . . . . . . . . . . . . . . . . . . . . . . . . . . 70

3.3 Geometry of the range determination for the ICP-EKF algorithm. . . . . . . 73

3.4 Flowchart of the ICP-EKF algorithm. . . . . . . . . . . . . . . . . . . . . . . 75

3.5 Matching segment determination. . . . . . . . . . . . . . . . . . . . . . . . . . 78

3.6 The generated map using the proposed map-building algorithm for the 4WDvehicle trials. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82

3.7 Observed and predicted ranges in the underground tunnel. . . . . . . . . . . . 83

3.8 ICP-EKF algorithm illustration. . . . . . . . . . . . . . . . . . . . . . . . . . 84

3.9 ICP-EKF estimated path and orientation of the 4WD vehicle for trial #1. . . 85

3.10 The pose errors and 2σ confidence bounds for 4WD trial #1. . . . . . . . . . 86

3.11 Range innovation and confidence bounds for 4WD trial #1. . . . . . . . . . . 86

3.12 ICP updates in the ICP-EKF algorithm for 4WD trial #1. . . . . . . . . . . 87

3.13 The wheel slip, the steering skid of the 4WD vehicle and their standarddeviations estimated by the ICP-EKF algorithm for trial #1. . . . . . . . . . 88

3.14 ICP-EKF estimated path and orientation of the 4WD vehicle for trial #2. . . 90

xv

3.15 The pose errors and 2σ confidence bounds for 4WD trial #2. . . . . . . . . . 91

3.16 Range innovation and confidence bounds for 4WD trial #2. . . . . . . . . . . 91

3.17 ICP updates in the ICP-EKF algorithm for 4WD trial #2. . . . . . . . . . . 92

3.18 The wheel slip, the steering skid of the 4WD vehicle and their standarddeviations estimated by the ICP-EKF algorithm for trial #2. . . . . . . . . . 93

3.19 Landmark selection illustration. . . . . . . . . . . . . . . . . . . . . . . . . . . 99

3.20 3D plots of information contribution from potential landmarks. . . . . . . . . 100

3.21 3D plots of information contribution from potential landmarks (contd.). . . . 101

3.22 Artificial landmark-augmented ICP-EKF estimated path and orientation ofthe LHD for trial #1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103

3.23 The pose errors and 2σ confidence bounds for the artificial landmark-augmentedICP-EKF algorithm for LHD trial #1. . . . . . . . . . . . . . . . . . . . . . . 104

3.24 Range innovation and confidence bounds in the artificial landmark-augmentedICP-EKF algorithm for LHD trial #1. . . . . . . . . . . . . . . . . . . . . . . 105

3.25 ICP updates in the artificial landmark-augmented ICP-EKF algorithm forLHD trial #1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105

3.26 The wheel radius, the front slip angle and their standard deviations estimatedby the artificial landmark-augmented ICP-EKF algorithm for LHD trial #1. . 106

3.27 The rear slip angle, the shaping state and their standard deviations estimatedby the artificial landmark-augmented ICP-EKF algorithm for LHD trial #1. . 107

3.28 Artificial landmark-augmented ICP-EKF estimated path and orientation ofthe LHD for trial #2. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109

3.29 The pose errors and 2σ confidence bounds for the artificial landmark-augmentedICP-EKF algorithm for LHD trial #2. . . . . . . . . . . . . . . . . . . . . . . 110

3.30 Range innovation and confidence bounds in the artificial landmark-augmentedICP-EKF algorithm for LHD trial #2. . . . . . . . . . . . . . . . . . . . . . . 110

3.31 ICP updates in the artificial landmark-augmented ICP-EKF algorithm forLHD trial #2. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111

3.32 The wheel radius, the front slip angle and their standard deviations estimatedby the artificial landmark-augmented ICP-EKF algorithm for LHD trial #2. . 112

3.33 The rear slip angle, the shaping state and their standard deviations estimatedby the landmark-augmented ICP-EKF algorithm for LHD trial #2. . . . . . . 113

4.1 Attneave’s cat. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117

4.2 1D scale space example. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120

4.3 Curvature definition. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122

4.4 Laser scan segmentation illustration. . . . . . . . . . . . . . . . . . . . . . . . 125

4.5 Laser scan segmentation illustration (contd.). . . . . . . . . . . . . . . . . . . 126

4.6 Errors with sub-sampling. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128

xvi List of figures

4.7 Errors with sub-sampling (contd.). . . . . . . . . . . . . . . . . . . . . . . . . 129

4.8 Dominant points characterised by curvature extrema: Example 1. . . . . . . . 132

4.9 Dominant points characterised by curvature extrema: Example 1 (contd.). . . 133

4.10 Dominant points characterised by curvature extrema: Example 2. . . . . . . . 134

4.11 Dominant points characterised by curvature extrema: Example 2 (contd.). . . 135

4.12 Scale space map of curvature extrema for Example 1. . . . . . . . . . . . . . . 136

4.13 Scale space map of curvature extrema for Example 2. . . . . . . . . . . . . . . 137

4.14 Dominant points characterised by curvature extrema shown across consecu-tive scans and corresponding scale space maps: Example 3. . . . . . . . . . . 138

4.15 Dominant points characterised by curvature extrema shown across consecu-tive scans and corresponding scale space maps: Example 4. . . . . . . . . . . 139

4.16 Illustration of the clustering process. . . . . . . . . . . . . . . . . . . . . . . . 140

4.17 Illustration of the clustering process (contd.). . . . . . . . . . . . . . . . . . . 141

4.18 Centroids of the cluster. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142

4.19 The extracted natural landmarks for LHD trial #1. . . . . . . . . . . . . . . . 143

4.20 Natural landmarks obtained in the circular section of the tunnel for LHDtrial #1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143

5.1 Natural landmark-augmented ICP-EKF estimated path of the LHD for trials#1 and #2. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151

5.2 Natural landmark-augmented ICP-EKF estimated orientation of the LHD fortrials #1 and #2. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152

5.3 Natural landmark-augmented ICP-EKF pose errors and 2σ confidence boundsfor LHD trials #1 and #2. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153

5.4 Comparison of the pose errors for LHD trials #1 and #2 with and withoutnatural landmark augmentation. . . . . . . . . . . . . . . . . . . . . . . . . . 154

5.5 Natural landmark-augmented ICP-EKF range innovation sequences and con-fidence bounds for LHD trials #1 and #2. . . . . . . . . . . . . . . . . . . . . 155

5.6 ICP updates in the natural landmark-augmented ICP-EKF algorithm forLHD trials #1 and #2. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 156

5.7 Range and bearing innovation sequences for the natural landmarks matchedduring LHD trials #1 and #2. . . . . . . . . . . . . . . . . . . . . . . . . . . 157

5.8 The wheel radii of the LHD and their standard deviations estimated by thenatural landmark-augmented ICP-EKF algorithm for trials #1 and #2. . . . 158

5.9 The front slip angles of the LHD and their standard deviations estimated bythe natural landmark-augmented ICP-EKF algorithm for trials #1 and #2. . 159

5.10 The rear slip angles of the LHD and their standard deviations estimated bythe natural landmark-augmented ICP-EKF algorithm for trials #1 and #2. . 160

xvii

5.11 The shaping states of the LHD and their standard deviations estimated bythe natural landmark-augmented ICP-EKF algorithm for trials #1 and #2. . 161

5.12 Test site of the utility vehicle field trial. . . . . . . . . . . . . . . . . . . . . . 163

5.13 The utility trial vehicle and the sensor suite. . . . . . . . . . . . . . . . . . . . 164

5.14 Speed and steer angle outputs of the utility vehicle after processing. . . . . . 165

5.15 GPS and encoder derived path and orientation obtained for the utility vehicletrial. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169

5.16 The laser scans and the extracted natural landmarks pertaining to the utilityvehicle trial. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 170

5.17 Natural landmark estimated path and orientation of the utility vehicle. . . . . 171

5.18 Pose errors and 2σ confidence bounds for the utility vehicle trial. . . . . . . . 172

5.19 Utility vehicle absolute pose errors. . . . . . . . . . . . . . . . . . . . . . . . . 172

5.20 Range and bearing innovation sequences for the natural landmarks matchedduring the utility vehicle trial. . . . . . . . . . . . . . . . . . . . . . . . . . . . 173

xviii

List of Tables

2.1 Vehicle Position Estimation Sensors . . . . . . . . . . . . . . . . . . . . . . . 10

2.2 Sensor Specifications for 4WD Trials . . . . . . . . . . . . . . . . . . . . . . . 22

Chapter 1

Introduction

1.1 The Aims and Objectives

THE aim of this thesis is to develop reliable and robust terrain aided localisation systemsfor autonomous land vehicles operating in unstructured, expansive and harsh environ-

ments. The resulting localisation system should be able to estimate the position of thevehicles to sub-metre accuracy and also minimise the requirement to add infrastructure tothe operating environment.

Localisation is defined as the ability of a vehicle to determine its two-dimensional position(xv,yv) and orientation (φv) within the operating environment. For autonomous vehiclelocalisation and in the context of this thesis, the definitions of the salient terms in the aboveaim are:

• Reliability denotes the ability to deliver precise vehicle estimates over many trials ina variety of operating conditions. Thus reliability encompasses both accuracy andrepeatability.

• Robustness indicates the competence to cope with external unmodelled disturbances.

• Unstructured implies a physical environment that does not have much regular structureor layout.

• An expansive environment is one which is much larger than the range of the sensorsavailable on the vehicle.

• Harshness refers to the uneven undulatory terrain of the operating environment.

The objectives of this thesis are:

• To develop algorithms that will guarantee reliable and robust localisation with partic-ular attention to challenges imposed by the operating environment.

• To investigate the feasibility of utilising landmarks that are naturally available in theoperating environment with the intention of minimising modifications to the environ-ment. Towards this, develop an algorithm that is capable of reliably detecting andextracting natural landmarks present in the environment.

2 Introduction

• To develop an algorithmic framework that allows the utilisation of measurements (ob-servations) from multiple sensors and has the potential to use measurements thatprovide maximum information in reducing localisation error. This requires an anal-ysis and understanding of the limitations of employing landmark-based localisationsystems in unstructured environments.

• To provide a demonstration of the algorithms developed using real data obtained fromfield trials conducted under varying operating conditions with attention to practicalconsiderations.

• To examine and demonstrate the feasibility of these algorithms in other autonomousland vehicle applications.

1.2 Motivation and Background

There is a strong case for the use of autonomous systems in various domains, including plan-etary exploration, mining, nuclear waste remediation, military reconnaissance, agricultureand construction [143],[197],[238],[30],[193],[198] with potential advantages ranging from in-creased safety to productivity benefits to environmental reasons. The general problem ofautonomous vehicle navigation can be summarised by the three questions: “Where am I?”,“Where am I going?”, “How do I get there?” [145]. The first question is termed localisation,the second as goal recognition and the third as path planning. Localisation is the processof determining the position and orientation (hereafter collectively referred to as the pose)of the vehicle within the operating environment. To realise the second and third tasks,given uncertainty in control and sensing, localisation is a fundamental ability required of anautonomous vehicle.

1.2.1 Sensor-based Localisation

The problem of autonomous vehicle localisation has received considerable attention fromroboticists in recent years and many methods have been proposed. These methods vary sig-nificantly, depending on the environment in which the vehicle is to navigate and the type ofsensors that are available. A survey of the vast body of literature [72],[48],[115],[26],[239],[20]on robot localisation indicates that the proposed approaches can be broadly classified as rel-ative and absolute methods. Most implementations of the localisation algorithms combinethe above two methods.

Dead-reckoning (derived from deduced reckoning) is the process of determining the positionof a vehicle by incrementally integrating motion information over a period of time. Odom-etry and Inertial Navigation System (INS) are the widely used dead-reckoning methodsfor vehicle positioning. Odometric methods use encoders to measure wheel rotation and/orsteering orientation. An inertial navigation system makes measurements of the accelerationsand attitude rates of the vehicle using an inertial measurement unit (IMU) consisting of gy-roscopes and accelerometers. Both odometry and inertial sensing provide high frequency,high bandwidth data.

The drawback of odometry is that the estimated vehicle position at a given instant dependson the previous estimate. This makes it difficult to eliminate errors associated with theprevious cycle due to sensor inaccuracies, the assumption that the heading remains constantover the sampling interval, wheel slippage and quantisation effects. As a consequence, the

1.2 Motivation and Background 3

vehicle pose (position and especially the orientation) becomes less and less certain andthe errors associated with the pose grow without bound. When the information from anIMU is appropriately integrated, the INS can be used to determine vehicle pose. However,integration of these noisy signals will result in a gradual growth or drift in pose estimateerror.

The major sources of dead-reckoning errors can be grouped into two categories namelysystematic and non-systematic errors [25]. Systematic errors accumulate constantly overtime due to unequal wheel diameters, misalignment of wheels, encoder sampling rates, driftsassociated with time etc. whereas non-systematic errors are as a result of wheel slippage,uneven terrain conditions and may occur unexpectedly. For a vehicle navigating on uneventerrain the non-systematic errors are more dominant than the systematic errors. Due to theaccumulation of errors in relative sensor-based methods, they need to be periodically reset byapplying external corrections from absolute positioning methods. Consequently, odometryand INS are almost always used in conjunction with some absolute position sensing system.

Examples of absolute positioning systems include beacons placed at known locations in theenvironment in combination with a sensor on board the vehicle, for example a sonar or alaser, or Global Positioning System (GPS) reported fixes. Such absolute systems can then beused in conjunction with odometry/INS to provide a non-divergent estimate of the vehicleposition. The resetting may also be accomplished by utilising an a priori map and/ornatural features1 in place of artificial landmarks. A typical navigation loop consists ofhigh frequency dead-reckoning sensors that predict vehicle pose and low frequency absolutesensors that provide external aiding information to bound positioning errors.

Localisation based on a combination of relative and absolute methods can be subdividedinto three basic categories namely [99]:

• Landmark-based Methods:There are several definitions of landmarks reported in the literature [179],[149],[147].In the context of this thesis, landmarks are taken to mean a localised, stationary, two-dimensional physical feature that the vehicle sensors can reliably and efficiently extractand recognise for determining vehicle position. The major drawback of landmark-based approaches is that the location of the landmarks must be known a priori. Inparticular, artificial landmark approaches require careful engineering of the environ-ment whereas natural landmark approaches require reliable feature extraction algo-rithms.

• Iconic Matching Methods:Iconic methods do not require the extraction of geometric features. The matchingworks directly on sensed data rather than on a small set of features, thus eliminatingthe need to determine what constitutes a landmark and to extract features. Iconicmethods attempt to globally minimise the discrepancies between sensed data and amodel of the environment. Because the search is confined to small perturbations ofthe sensor scans, it is computationally efficient. The drawback of these methods isthat they are sensitive to bad matches and incorrect error models.

• Behaviour-based Methods:The central theme of behaviour-based robotics is to directly couple a robot’s sen-sors and actuators so as to obviate the need of maintaining an environment model.Behavioural approaches rely on the interaction of the robot with its environment in

1“Landmarks” and “ Features” are used interchangeably except when an explicit distinction betweennatural and artificial landmarks is made.

4 Introduction

which it is to navigate. Thus their ability to localise geometrically is limited since theirnavigation capability is implicitly dependent on their sensor/action history. There aresome advantages in using a behaviour-based scheme. These schemes are more re-sponsive to the environment and their performance degrades gradually and gracefullyin the case of system failure rather than abruptly and catastrophically. Conversely,the interactions are not easily modelled, are less predictable and so are difficult tounderstand and modify.

Achieving localisation in structured environments is simpler than that in unstructured en-vironments. In the context of this thesis, structured environments are taken to mean even-terrain feature-rich domains such as offices. An unstructured environment is taken to meanenvironments with uneven terrain susceptible to vehicle slipping and skidding and few dis-tinguishable naturally occurring landmarks. From an autonomous vehicle navigation pointof view, an unstructured environment presents the following unique challenges:

• Errors introduced due to distance travelled can be significant and unpredictable. Thisis a direct consequence of the undulatory nature of the terrain of travel and the uncer-tainties introduced into sensor data. Errors introduced by irregular terrain conditionscan not be predicted or modelled. Higher vehicle speeds in such environments alsoexacerbate these errors.

• For large unstructured environments, it is extremely difficult to construct perfect mapsfor navigation as compared to small structured domains where construction of suchmaps is possible due to the underlying polygonal structure of the environment.

• Reliable detection and association of both artificial and natural landmark measure-ments from corrupted sensor data is difficult. The landmark detection and data as-sociation algorithms should be robust to false alarms so as to prevent catastrophicfailure. Occlusions and outliers also complicate the process of determining a legiti-mate landmark.

• Sensor suite selection should take into consideration the challenges posed by the un-structured domain. For example, GPS can not be employed underground since therequired line-of-sight can not be maintained. Furthermore, sensors that are commonlyemployed in a structured environment may not function properly in an unstructuredenvironment due to various external environmental factors like humidity, dust andheat. Other detrimental effects include excessive vibration and pitching and rollingmotions experienced by vehicles navigating in such domains. Sufficient care should betaken to ensure that the sensors have satisfactory range and attenuation to be usefulin unstructured environments.

These challenges make it comparatively difficult to realise successful navigation in unstruc-tured environments.

In mining, automation of underground operations is an important goal for reasons of person-nel safety and lower operational costs. With automation of underground vehicles, productiv-ity benefits come from the increase in operating hours, reduced wear and tear, eliminationof travelling time to the machine and greatly reduced operator fatigue. This thesis is awider part of a project aimed at achieving autonomy of Load-Haul-Dump (LHD) vehiclesin underground mine tunnels. It was funded by the Australian Mineral Industries Research

1.3 Contributions of the Thesis 5

Association (AMIRA) and was a collaborative venture between the Commonwealth Sci-entific Industrial Research Organisation (CSIRO) Manufacturing Science and Technology(MST) automation group, CSIRO Exploration and Mining (E & M) and the University ofSydney’s Australian Centre for Field Robotics (ACFR) conducted under the auspices of theCooperative Research Centre for Mining Technology and Equipment (CMTE).

Motivated by the above factors, this thesis develops localisation algorithms for autonomousnavigation of vehicles operating in unstructured environments, like underground mines,by drawing upon the knowledge gained from the collaborative venture and work performedalongside underground miners and mining researchers. The localisation algorithms describedin this thesis fall into the iconic and landmark-based category but are sufficiently generic tobe used on a variety of other autonomous land vehicles. These methods were chosen becauseof their efficiency and robustness. The algorithms also seek to minimise the engineering ormodification of the environment, such as adding artificial landmarks or other infrastructure.This is a key driver in the practical implementation of a localisation algorithm.

In this thesis, observations from natural and/or artificial landmarks need to be combinedwith dead-reckoning estimates to estimate vehicle pose. This requires a framework withinwhich the sensor data can be fused synergically. The Kalman filter is a statistically efficientway of achieving this requirement. The Kalman filter has been widely employed to fuse low-level redundant data to provide an estimate of the states of a system [162] and is employedas the sensor fusion and estimation tool in this thesis. Sections A.1 and A.2 of the Appendix,respectively, describe the linear Kalman filter and the extended Kalman filter (EKF) froma navigation point of view to introduce notations that will be used throughout this thesis.

1.3 Contributions of the Thesis

This thesis makes the following theoretical and practical contributions:

• The development and demonstration of a robust framework that efficiently reducesinfrastructure by combining measurements from artificial and natural landmarks toreliably estimate vehicle position.

• The development of an iconic map-based localisation algorithm for a four-wheel drivevehicle and a Load-Haul-Dump truck operating in mine tunnels. The localisationalgorithm entails the development of a map-building algorithm.

• The analysis, development and adaptation of a view-invariant multiscale natural land-mark extraction algorithm that is robust with respect to sensor noise.

• The development of a metric for the evaluation of information content of measurementstowards the selection of a landmark from a given set of landmarks.

• The demonstration of a natural landmark localisation algorithm on a utility vehicleobviating the need for artificial landmarks.

• The implementation of an artificial landmark localisation algorithm that facilitates thecomparison and analysis of the proposed algorithms, by providing the ground truth.

6 Introduction

1.4 The Structure of the Thesis

Chapter 2 presents an artificial landmark-based localisation algorithm for two vehi-cles - a four-wheel drive vehicle and a Load-Haul-Dump truck. The algorithm is basedon measurements obtained from a bearing-only laser that returns bearing values toartificial landmarks strategically placed in the operating environment of the vehicles.

The sensor suite, the calibration trials and the hardware and software aspects of thedata logging system for the four-wheel drive vehicle are first described. This is followedby the description of the process and observation models and observation validationprocedures that are required for the successful implementation of the localisation algo-rithms. The corresponding experimental results for two field trials for the four-wheeldrive vehicle and the load-haul-dump truck are then presented and discussed.

A critical overview of various landmark-based navigation methods and previous at-tempts to automate underground mining vehicles is also included.

Chapter 3 develops a map-based iconic algorithm for localisation of the four-wheeldrive vehicle and the load-haul-dump truck. The proposed algorithm requires thedevelopment of a map-building algorithm. Both the map-building and localisationalgorithms use data from a time-of-flight range and bearing laser.

The shortcomings of the proposed iconic localisation algorithm are identified and astrategy to overcome these deficiencies is proposed. This leads to the developmentof a metric that embodies the information contained in a measurement. Based onthis metric, the map-based iconic localisation algorithm is augmented with artificiallandmarks. Experimental results of the proposed map-based localisation algorithm forboth the vehicles are discussed.

An overview of map-based navigation methods is also provided.

Chapter 4 is concerned with the development of a multiscale natural landmark ex-traction algorithm. The Chapter starts out with a comprehensive review of variousmultiscale description techniques with particular attention to the concept of scale andits selection. The proposed algorithm has the advantage of examining laser rangescans at varying levels of detail to arrive at a stable natural landmark. The needto decide upon what constitutes a landmark and the motivation behind the selectionof a primitive for the extraction of a landmark in the operating environment are alsoelaborated. The proposed algorithmic procedures lead to the construction of a naturallandmark map.

Chapter 5 develops a unified algorithmic framework for localisation of autonomousvehicles operating in unstructured environments by combining the ideas presented inChapters 3 and 4. By using measurements from both artificial and natural landmarks,with the aid of the information metric developed in Chapter 3 and the natural land-mark map and extraction algorithm detailed in Chapter 4, a minimal infrastructurelocalisation algorithm is demonstrated on two field trials for localisation of the load-haul-dump truck. The developed framework is shown to deliver reliable vehicle poseestimates proving the robustness of the proposed algorithms.

The Chapter then extends the multiscale natural landmark algorithm to demonstratelocalisation of a utility vehicle without the use of any external infrastructure.

1.4 The Structure of the Thesis 7

Chapter 6 provides a summary and lists the principal results of the thesis. Sugges-tions for further research and the extension of the theories developed in this thesis toother areas are also briefly discussed.