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Real Time 3D Mapping for Small Wall Climbing Robots

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Real Time 3D Mapping for Small Wall Climbing Robots Real Time 3D Mapping for Small Wall Climbing Robots Blair David Sidney Howarth A thesis submitted in fulfilment of the requirements for the degree of Doctor of Philosophy School of Mechanical and Manufacturing Engineering The University of New South Wales February 2012 THE UNIVERSITY OF NEW SOUTH WALES Thesis/Dissertation Sheet Surname or Family name: HOWARTH First name: BLAIR Other name/s: DAVID SIDNEY Abbreviation for degree as given in the University calendar: MTRN (1662) School: MECHANICAL AND MANUFACTURING Faculty: ENGINEERING Title: REAL TIME 3D MAPPING FOR SMALL WALL CLIMBING ROBOTS Abstract 350 words maximum: Small wall climbing robots are useful because they can access difficult environments which preclude the use of more traditional mobile robot configurations. This could include an industrial plant or collapsed building which contains numerous obstacles and enclosed spaces. These robots are very agile and they can move fully through three dimensional (3D) space by attaching to nearby surfaces. For autonomous operation, they need the ability to map their environment to allow navigation and motion planning between footholds. This surface mapping must be performed onboard as line-of-sight and wireless communication may not always be available. As most of the methods used for robotic mapping and navigation were developed for two dimensional usage, they do not scale well or generalise for 3D operation. Wall climbing robots require a 3D map of nearby surfaces to facilitate navigation between footholds. However, no suitable mapping method currently exists. A 3D surface mapping methodology is presented in this thesis to meet this need. The presented 3D mapping method is based on the fusion of range and vision information in a novel fashion. Sparse scans from a laser range finder and a low resolution camera are used, along with feature extraction, to significantly reduce the computational cost. These features are then grouped together to act as a basis for the surface fitting. Planar surfaces, with full uncertainty, are generated from the grouped range features with the image features being used to generate planar polygon boundaries. These surfaces are then merged together to build a 3D map surrounding a particular foothold position. Both experimental and simulated datasets are used to validate the presented surface mapping method. The surface fitting error is satisfactory and within the required tolerances of a wall climbing robot prototype. An analysis of the computational cost, along with experimental runtime results, indicates that onboard real time operation is also achievable. The presented surface mapping methodology will therefore allow small wall climbing robots to generate real time 3D environmental maps. This is an important step towards achieving autonomous operation. Declaration relating to disposition of project thesis/dissertation I hereby grant to the University of New South Wales or its agents the right to archive and to make available my thesis or dissertation in whole or in part in the University libraries in all forms of media, now or here after known, subject to the provisions of the Copyright Act 1968. I retain all property rights, such as patent rights. I also retain the right to use in future works (such as articles or books) all or part of this thesis or dissertation. I also authorise University Microfilms to use the 350 word abstract of my thesis in Dissertation Abstracts International (this is applicable to doctoral theses only). 11 Feb 2012 ……………………………………………………… ………………………………………………… ……….………………...…….… Signature Witness Date The University recognises that there may be exceptional circumstances requiring restrictions on copying or conditions on use. Requests for restriction for a period of up to 2 years must be made in writing. Requests for a longer period of restriction may be considered in exceptional circumstances and require the approval of the Dean of Graduate Research. FOR OFFICE USE ONLY Date of completion of requirements for Award: THIS SHEET IS TO BE GLUED TO THE INSIDE FRONT COVER OF THE THESIS Abstract Small wall climbing robots are useful because they can access difficult environments which preclude the use of more traditional mobile robot configurations. This could include an industrial plant or collapsed building which contains numerous obstacles and enclosed spaces. These robots are very agile and they can move fully through three dimensional (3D) space by attaching to nearby surfaces. For autonomous oper- ation, they need the ability to map their environment to allow navigation and motion planning between footholds. This surface mapping must be performed onboard as line-of-sight and wireless communication may not always be available. As most of the methods used for robotic mapping and navigation were developed for two dimensional usage, they do not scale well or generalise for 3D operation. Wall climbing robots require a 3D map of nearby surfaces to facilitate navigation between footholds. However, no suitable mapping method currently exists. A 3D surface mapping methodology is presented in this thesis to meet this need. The presented 3D mapping method is based on the fusion of range and vision information in a novel fashion. Sparse scans from a laser range finder and a low resolution camera are used, along with feature extraction, to significantly reduce the computational cost. These features are then grouped together to act as a basis for the surface fitting. Planar surfaces, with full uncertainty, are generated from the grouped range features with the image features being used to generate planar polygon boundaries. These surfaces are then merged together to build a 3D map surrounding a particular foothold position. Both experimental and simulated datasets are used to validate the presented surface mapping method. The surface fitting error is satisfactory and within the required tolerances of a wall climbing robot prototype. An analysis of the compu- tational cost, along with experimental runtime results, indicates that onboard real time operation is also achievable. The presented surface mapping methodology will therefore allow small wall climbing robots to generate real time 3D environmental maps. This is an important step towards achieving autonomous operation. ii Acknowledgements There are many people that I would like to thank for helping me through the past four years of my PhD studies. The process has been long, challenging and at times difficult but overall it has been a rewarding one, especially now that the end is in sight! To my PhD supervisor, Associate Professor Jayantha Katupitiya, you have been not only a mentor over the past four years, but a friend too. Thank you for your guidance and support. You have been able to provide a good balance between giving me the freedom to work independently whilst always being available to assist when required. Thanks also to my co-supervisor, Dr Jose Guivant, for your valuable insights. Your ability to provide a fresh perspective has been very valuable. I would also like to acknowledge the financial support of the ARC Centre of Excellence programme, along with the APA scholarship programme, who have helped fund my studies. A massive thank you must go to my parents, Graeme and Shirley, and my two sisters, Louise and Emily. You have given me so much love and support over the years. You are always interested in what I am doing and how I am progressing and I am so grateful to have such a wonderful and loving family. Being able to talk or Skype regularly has made studying away from home that much easier. To Avi, my incredible girlfriend, I want to say a big thanks for all your support and encouragement. You always seem to be able to push me to be better and you have the amazing ability to make me smile. Throughout my PhD studies I have worked alongside many fine students, both postgraduate and undergraduate. Thanks especially to my fellow PhD candidates, James and Mark. I have enjoyed our discussions on all manner of issues and you have always been able to provide fresh ideas and feedback. Basser College was my home for the majority of my studies here at UNSW. After moving from New Zealand I didn’t know many people in Sydney. Moving into Basser as a Tutor/Resident Fellow was one of the best decisions I ever made. I felt so welcomed and met so many wonderful people in what has been, and always will be, the best residential college at UNSW. I have made many new friends and I have immensely enjoyed the good natured trans-tasman rivalry. A big shout out to my iii fellow Basser tutors, especially to Evan, El, Sarah and Lauren. A special mention must go to Dr Geoff Treloar. Your guidance and support as head of Basser College was most valuable but you were also a good friend. Basser is now poorer without your stewardship and your many words of wisdom. Thanks also to my physio Mark for putting up with me over the last four years and fixing up my neck, back and myriad other injuries. A final thanks to Geoff, Mark and Louise for proof reading and providing valuable feedback on my thesis. Your help has been invaluable in polishing the final version of this thesis and I am very grateful for your time and effort. iv Declaration “I hereby declare that this submission is my own work and to the best of my knowledge it contains no materials previously published or written by another person, or substantial proportions of material which have been accepted for the award of any other degree or diploma at UNSW or any other educational institution, except where due acknowl-
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