Skip to content | Change text size

Online publications of Lindsay Kleeman

INDEX:
Picture of Lindsay Kleeman

Associate Professor Lindsay Kleeman 
Lindsay.Kleeman[AT]monash.edu
Intelligent Robotics Research Centre
Department Electrical & Computer Systems Engineering, 
Monash University, VIC 3800 AUSTRALIA 
Tel : +61 3 99053491  Fax : +61 3 99053454 
Keywords : robotics, sonar, ultrasonic, VLSI, sensing, localisation, logic, digital, self-timed, asynchronous.

© Copyright Notice
This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the authors and other copyright holders.

For IEEE publications, the following copyright notice applies. © 1992-2013 IEEE. Permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. 

Awards:

2017:            Raymond A Jarvis Best Paper Award: Timo Stoffregen and Lindsay Kleeman, “Simultaneous Optical Flow and Segmentation (SOFAS) using a Dynamic Vision Sensor”,
Australasian Conference Robotics and Automation (ACRA), Dec 11-13, 2017.

2017:            Best Student Paper Award: Yanming Pei and Lindsay Kleeman, “A Novel Odometry Model for Wheeled Mobile Robots Incorporating Linear Acceleration”, 
IEEE International Conference Mechatronics and Automation Aug 6-9, 2017.

2012:            Best Student Paper Award, International Conference on Biomedical Electronics and Devices, Vilamoura Portugal Feb 2012 for paper entitled:
“Mobile, Real-Time Simulator for a Cortical Visual Prosthesis”, by Horace Josh, Benedict Yong and Lindsay Kleeman.

2008:             Award for Excellence in Physical Sciences and Mathematics, presented to Lindsay Kleeman (and other authors) for
Springer Handbook of Robotics, American Publishers Awards, Association of American Publishers, Inc.

2007:            Best Paper Prize at the Australasian Conference on Robotics and Automation 2007 awarded to the paper by
Alan Zhang and Lindsay Kleeman, “Robust Appearance Based Visual Route Following in Large Scale Outdoor Environments”.

2007:            Douglas Lampard Medal for the Best PhD Thesis Dept ECSE Monash University 2006 awarded to Albert Diosi
supervised by Lindsay Kleeman for his thesis “Laser rangefinder and advanced sonar based simultaneous localisation and mapping for mobile robots”.

2005:            Dean of Engineering Excellence Award for Research in the Department of Electrical and Computer Systems Engineering, Monash University.

2000:            Nakamura prize for the best paper at International Conference on Intelligent Robots and Systems 1999 (IROS’99)
"Fast and accurate sonar trackers using double pulse coding" by Lindsay Kleeman

1996:            Highly Commended Award at the Engineering Excellence Awards on the 21 August. 1996
for the development of a 3D shape measurement system.

1984:             IEEE Centennial Regional Student Award for Region 10 (India, Asia, Australasia, Pacific).  One of 10 awarded internationally to
student members of the IEEE to participate in the 1884/1984 Centennial celebrations in Boston, U.S.A.

1983:            University Medal in Mathematics.  University of Newcastle, Australia

                      Australian Computer Research Board Postgraduate Scholarship (2 offered in Australia)

1982:            Institute of Engineers Australia Prize for the highest pass in Bachelor of Engineering (JMC Corlett medal).

                      University Medal in Electrical Engineering, University of Newcastle, Australia

1980:            Philosophy I Staff Prize. University of Newcastle, Australia

                      3rd Year Mathematics Prize. University of Newcastle, Australia

1979:            BHP Prize in Electrical Engineering. University of Newcastle, Australia

                      2nd Year Mathematics Prize.University of Newcastle, Australia

                      Apollo Commemorative Prize in 2nd Year Physics.University of Newcastle, Australia

1978:            Les Gibbs Prize for creative design in Engineering I.  University of Newcastle, Australia

                      Mortimer Temple prize in Mathematics I. University of Newcastle, Australia

            

Selected Papers:

Journal Papers:

  • Horace Josh and Lindsay Kleeman, “A Novel Hardware Plane Fitting Implementation and Applications for Bionic Vision”, International Journal of Machine Vision and Applications, Springer, Oct 2016, Vol 27, Issue 7, pp 967-982.  
  • Mohammed Ziaur Rahman, Lindsay Kleeman and AHM Ashfak Habib, “Recursive Approach to the Design of a Parallel Self-Timed Adder”, IEEE Transactions on Very Large Scale Integration (VLSI) Systems, vol.23, no.1, pp.213-217, Jan. 2015.
  • Jean-Michel Redouté, Damien Browne, David Fitrio, Arthur Lowery, Lindsay Kleeman, “A reduced data bandwidth integrated electrode driver for visual intracortical neural stimulation in high voltage CMOS”, Microelectronics Journal, Volume 44, Issue 4, April 2013, Pages 277–282.
  • Kang Lim Yoong, Lindsay Kleeman and Tom Drummond, “Algorithmic methodologies for FPGA-based vision”  International Journal of Machine Vision and Applications, Springer, ISSN 0932-8092  2013.
  • D C Browne and L Kleeman, “A Sonar Ring with Continuous Matched Filtering and Dynamically Switched Templates”, Robotica, v 30, n 6, p 891-912, October 2012
  • F Tungadi and L Kleeman, “Autonomous Loop Exploration and SLAM with Fusion of Advanced Sonar and Laser Polar Scan Matching”, Robotica, Cambridge University Press 2012, DOI:10.1017/S0263574711000348, Vol 30 Issue 01, 2012, pp 91 - 105.
  • D C Browne and L Kleeman, “A Double Refresh Rate Sonar Ring with FPGA Based Continuous Matched Filtering” , Robotica, Vol 30 Issue 07 Dec 2012, pp 1051 – 1062.
  • F Tungadi and L Kleeman, “Discovering and Restoring Changes in Object Positions using an Autonomous Robot with Laser Rangefinders”, Robotics and Autonomous Systems (A) vol 59 no 6 (2011) pp 428–443.
  • M Ooi, E K J Sim, Y C Kuang, L Kleeman, C. Chan and S Demidenko, “Getting More from the Semiconductor Test: Data Mining with Defect Cluster Extraction”, IEEE Transactions on Instrumentation & Measurement, Vol 60 No 10, October 2011, pp 3300-3317.
  • W H Li and L Kleeman, “Segmentation and Modelling of Visually Symmetric Objects by Robot Actions”, IJRR (A*), Vol 30 Number 9, Aug 2011, pp1124-1142.
  • A Zhang and L Kleeman, “Robust Appearance Based Visual Route Following for Navigation in Large Scale Outdoor Environments”, International Journal Robotics Research, Vol. 28, No. 3, 331-356, March 2009 DOI: 10.1177/0278364908098412.
  • M Z Rahman and L Kleeman, “Paired Measurement Localization: A Robust Approach for Wireless Localization”,  IEEE  Transactions on Mobile Computing  2009.
  • W H Li, A M Zhang and L Kleeman “Bilateral Symmetry Detection for Real-time Robotics Applications”, International Journal Robotics Research, Vol. 27, No. 7, July 2008, pp. 785–814. DOI: 10.1177/0278364908092131.
  • A Diosi and L Kleeman, “Fast Laser Scan Matching using Polar Coordinates”, International Journal Robotics Research, Vol 26, No. 10, Oct 2007, pp 1125-1153.
  • R L Stewart, R A Russell and L Kleeman, “Modelling a Deposition Process in Collective Construction”, ELEKTRIK journal, Special issue on swarm robotics, Vol 15, No. 2, 2007, pp 227-255.
  • S. Fazli and L. Kleeman "Sensor Design and Signal Processing for an Advanced Sonar Ring", Robotica, Volume 24, Issue 04, July 2006, pp 433-446..
  • S. Fazli and L. Kleeman, "Simultaneous Landmark Classification, Localisation and Map Building for an Advanced Sonar Ring", Robotica 2006.
  • G. Taylor and L. Kleeman, "Stereoscopic Light Stripe Scanning: Interference Rejection, Error Minimization and Calibration", International Journal Robotics Research, Vol 23 No 12, Dec 2004, pp 1141-1156. multimedia extensions
  • L. Kleeman, "Advanced sonar with velocity compensation", International Journal Robotics Research, Vol 23 No 2. Feb 2004, pp 111-126.
  • R. A. Russell, G. Taylor, L. Kleeman and Anies Purnamadjaja, "Humanoid Robot Sensor Synergies", International Journal of Humanoid Robotics, Vol. 1, No. 2 2004, pp. 289-314.
  • K S Chong and L. Kleeman, ”Feature-based mapping in real, large scale environments using an ultrasonic array”, International Journal Robotics Research, Vol 18,  No. 1,  Jan 1999, pp. 3-19 PDF version.
  • K S Chong and L. Kleeman, "Mobile robot map building for an advanced sonar array and accurate odometry", International Journal Robotics Research. Vol 18, No. 1, Jan 1999, pp. 20-36.
  • L. Kleeman and R. Kuc, "Mobile robot sonar for target localization and classification", International Journal of Robotics Research, Volume 14, Number 4, August 1995, pp 295-318. PDF version
  • L. Kleeman, “Real time mobile robot sonar with interference rejection”, Sensor Review Vol 19, No. 3, 1999, pp. 214-221. PDF version.
  • L.Kleeman "The jitter model for metastability and its application to redundant synchronizers", IEEE Trans. Computers, Vol. 39, No. 7, pp. 930 - 942, July 1990.
  • L.Kleeman and A.Cantoni "Metastable behaviour in digital systems", IEEE Design and Test of Computers", Volume 4, No. 6, pp 4-19, December, 1987.

Conference Papers:

  • Timo Stoffregen and Lindsay Kleeman, “Simultaneous Optical Flow and Segmentation (SOFAS) using Dynamic Vision Sensor”, Australasian Conference Robotics and Automation (ACRA), 2017. winner of Best Student Paper Award.
  • Joshua Weberruss, Lindsay Kleeman, David Boland and Tom Drummond, “FPGA Acceleration of Multilevel ORB Feature Extraction for Computer Vision”, International Conference on Field-Programmable Logic and Applications, FPL 2017.
  • Yanming Pei and Lindsay Kleeman, “A Novel Odometry Model for Wheeled Mobile Robots Incorporating Linear Acceleration”, IEEE International Conference Mechatronics and Automation Aug 6-9, 2017. winner of Best Student Paper Award.
  • Yanming Pei and Lindsay Kleeman, “Mobile Robot Floor Classification using Motor Current and Accelerometer Measurements”,  IEEE 14th International Workshop on Advanced Motion Control (AMC16) Auckland, New Zealand 22-24 April 2016.
  • Joshua Weberruss, Lindsay Kleeman and Tom Drummond, “ORB Feature Extraction and Matching in Hardware”, Australasian Conference Robotics and Automation (ACRA), Canberra 2015.
  • Yanming Pei and Lindsay Kleeman, “Online Robot Odometry Calibration over Multiple Regions Classified by Floor Colour”, IEEE International Conference Mechatronics and Automation (ICMA) 2015 pp. 2589 - 2596, 2015.
  • Yanming Pei and Lindsay Kleeman, “Robot Calibration of Sensor Poses and Region Based Odometry Using Offline Optimisation of Map Information”, IEEE International Conference Information and Automation (ICIA) 2015, pp: 462 - 468, 2015. Finalist of best paper award.
  • Horace Josh, Collette Mann, Lindsay Kleeman and Wen Lik Dennis Lui, “Psychophysics Testing of Bionic Vision Image Processing Algorithms Using an FPGA Hatpack”, accepted for presentation ICIP 2013.
  • Wen Lik Dennis Lui, Damien Browne, Lindsay Kleeman, Tom Drummond and Wai Ho Li, “Transformative Reality: Improving bionic vision with robotic sensing”, 34th Annual International IEEE EMBS Conference, San Diego, USA Aug 2012.
  • H Josh, B Yong and L Kleeman, “Mobile, Real-time Simulator for a Cortical Visual Prosthesis”, BIODEVICES 2012 - Proceedings of the International Conference on Biomedical Electronics and Devices, p 37-46, Feb 2012, winner of Best Student Paper Award.
  • H Josh, B Yong and L Kleeman, “A Real-time FPGA-based Vision System for a Bionic Eye”,  ACRA 2011, Monash University, Melbourne Australia, Dec 7-9 2011, pp 1-8. 
  • Wen Lik Dennis Lui, Damien Browne, Lindsay Kleeman, Tom Drummond and Wai Ho Li, "Transformative Reality: Augmented Reality for Visual Prostheses", IEEE International Symposium on Mixed and Augmented Reality (ISMAR), Basel, Switzerland, 26-29 October 2011, pp. 253-254. ISBN 978-1-4577-2183-0
  • F Tungadi, W L Lui, L Kleeman and R A Jarvis, “Robust Online Map Merging System using Laser Scan Matching and Omnidirectional Vision”, IROS 2010. IEEE/RSJ International Conference on Intelligent Robots and Systems, Taipei, Taiwan October 18-22, 2010. 
  • M Ooi, E K J Sim, Y C Kuang, L Kleeman, C. Chan and S Demidenko “Automatic Defect Cluster Extraction for Semiconductor Wafers”, I2MTC2010 IEEE International Instrumentation and Measurement Technology Conference, Austin TX USA, May 3-6 2010,  pp.1024 - 1029
  • M Ooi, C Chan, W J Tee, Y C Kuang, L Kleeman, S Demidenko, “Fast and Accurate Automatic Defect Cluster Extraction for Semiconductor Wafers”, The 5th  IEEE International Symposium on Electronic Design, Test & Applications (DELTA 2010), Ho Chi Minh City, Vietnam, January 13-15, 2010, pp. 276 - 280
  • D Browne and L Kleeman, “An Advanced Sonar Ring Design with 48 Channels of Continuous Echo Processing using Matched Filters”, IROS 2009, St. Louis, Missouri, USA.
  • W H Li and L Kleeman, “Interactive Learning of Visually Symmetric Objects”, IROS 2009, St. Louis, Missouri, USA.
  • F Tungadi and L Kleeman, “Loop Exploration for SLAM with Fusion of Advanced Sonar Features and Laser Polar Scan Matching”, IROS 2009, St. Louis, Missouri, USA.
  • F Tungadi and L Kleeman, “Time Synchronisation and Calibration of Odometry and Range Sensors for High-Speed Mobile Robot Mapping”, ACRA 2008, Canberra.
  • W H Li and L Kleeman, “Autonomous Segmentation of Near-Symmetric Objects through Vision and Robotic Nudging” IROS, Nice France, September 2008, pp 3604-3609.
  • M Z Rahman and L Kleeman, “Self-Localization Schemes for Geographic Routing in Wireless Sensor Networks” 2008 IEEE 67th Vehicular Technology Conference: VTC2008-Spring 11–14 May 2008, Marina Bay, Singapore pp 71-75. 
  • A Zhang and L Kleeman, “Robust Appearance Based Visual Route Following in Large Scale Outdoor Environments”, ACRA 2007, Brisbane.  Winner best student paper award.
  • F Tungadi and L Kleeman, “Multiple Laser Polar Scan Matching with application to SLAM”, ACRA 2007, Brisbane.
  • L Kornienko and L Kleeman, “An Autonomous Human Body Parts Detector Using A Laser Range-Finder”, ACRA 2007, Brisbane.
  • P Chakravarty, A Zhang, R Jarvis and L Kleeman. “Anomaly Detection and Tracking for a Patrolling Robot”, ACRA 2007, Brisbane.
  • W H Li and L Kleeman, “Real Time Object Tracking using Reflectional Symmetry and Motion”, Proceedings IEEE/RSJ International Conference on Intelligent Robots and Systems 2006, pp 2798- 2803.
  • W H Li, A M Zhang and L Kleeman, “Real Time Detection and Segmentation of Reflectionally Symmetric Objects in Digital Images”, Proceedings IEEE/RSJ International Conference on Intelligent Robots and Systems 2006, pp 4867-4873.
  • A M Zhang and L Kleeman, “Topological Mapping Inspired by Techniques in DNA Sequence Alignment”, Proceedings IEEE/RSJ International Conference on Intelligent Robots and Systems 2006, pp 2754-2759.
  • L Kleeman and A Ohya, “The Design of a Transmitter with a Parabolic Conical Reflector for a Sonar Ring”, Australasian Conference on Robotics and Automation, Dec 2006, Auckland New Zealand.
  • A Zhang and L Kleeman, “A Panoramic Color Vision System for Following Ill-Structured Roads”, Australasian Conference on Robotics and Automation, Dec 2006, Auckland New Zealand.
  • W H Li and L Kleeman, “Fast Stereo Triangulation using Symmetry”, Australasian Conference on Robotics and Automation, Dec 2006, Auckland New Zealand.
  • W H Li, A M Zhang and L Kleeman, "Real Time Detection and Segmentation of Reflectionally Symmetric Objects in Digital Images", Proceedings 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2006.
  • W H Li and L Kleeman, "Real Time Object Tracking using Reflectional Symmetry and Motion", Proceedings 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2006.
  • A M Zhang and L Kleeman, "Topological Mapping Inspired by Techniques in DNA Sequence Alignment", Proceedings 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2006.
  • W H Li, A M Zhang and L Kleeman, "Fast Global Reflectional Symmetry Detection for Robotic Grasping and Visual Tracking" Australasian Conference on Robotics and Automation, Dec 2005, Sydney, Australia
  • A Diosi and L Kleeman, "Laser Scan Matching in Polar Coordinates with Application to SLAM", Proceedings IEEE/RSJ International Conference on Intelligent Robots and Systems 2005, pp 1439-1444.
  • A Diosi, G Taylor and L Kleeman, "Interactive SLAM using Laser and Advanced Sonar", Proceedings of the 2005 IEEE International Conference on Robotics and Automation Barcelona, Spain, April 2005, pp 1115-1120.
  • G Taylor and L Kleeman, "A Multiple Hypothesis Walking Person Tracker with Switched Dynamic Model", Proceedings of the Australasian Conference on Robotics and Automation, Dec 2004, Canberra, Australia. Video
  • S Fazli and L Kleeman, "A Low Sample Rate Real Time Advanced Sonar Ring", Proceedings of the Australasian Conference on Robotics and Automation, Dec 2004, Canberra, Australia.
  • G. Taylor and L. Kleeman, "Integration of Robust Visual Perception and Control for a Domestic Humanoid Robot", Proceedings 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems IROS2004, Sendai Japan pp1010-1015.
  • A. Diosi and L. Kleeman, "Advanced Sonar and Laser Range Finder Fusion for Simultaneous Localization and Mapping", Proceedings 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems IROS2004, Sendai Japan pp 1854-1859.
  • S. Fazli and L. Kleeman, "A Real Time Advanced Sonar Ring with Simultaneous Firing", Proceedings 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems IROS2004, Sendai Japan, pp. 1872-1877.
  • G. Taylor and L. Kleeman, "Hybrid Position-Based Visual Servoing with Online Calibration for a Humanoid Robot", Proceedings 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems IROS2004, Sendai Japan pp 686-691.
  • G. Taylor and L. Kleeman, "Fusion of Multimodal Visual Cues for Model-Based Object Tracking", Australiasian Conference on Robotics and Automation, Brisbane Dec 2003.
  • A Diosi and L. Kleeman, "Uncertainty of Line Segments Extracted from Static SICK PLS Laser", Australiasian Conference on Robotics and Automation Brisbane Dec 2003.
  • G. Taylor and L. Kleeman, "Robust Range Data Segmentation Using Geometric Primitives for Robotic Applications", Proceedings of the 5th IASTED International Conference on Signal and Image Processing August 13-15, Honolulu, Hawaii 2003, pp 467-472.
  • L. Kleeman, "Advanced Sonar and Odometry Error Modeling for Simultaneous Localisation and Map Building" Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Las Vegas 2003, pp 699-704.
  • G. Taylor and L. Kleeman, "Grasping unknown objects with a humanoid robot", Proceedings 2002 Australiasian Conference on Robotics and Automation Aukland 27-29 November 2002, pp. 191-196. VRML data available (low resolution 34k, medium resolution 1.5M, high resolution 6.1M) of laser stripe 3D data.
  • L. Kleeman, "On-the-fly classifying sonar with accurate range and bearing estimation" IEEE/RSJ International Conference on Intelligent Robots and Systems, 2002, pp.178-183.
  • G. Taylor, L. Kleeman and Ĺ. Wernersson, "Robust colour and range sensing for robotic applications using a stereoscopic light stripe scanner", IEEE/RSJ International Conference on Intelligent Robots and Systems, 2002, pp. 86-91. VRML data available (low resolution 34k, medium resolution 1.5M, high resolution 6.1M) of laser stripe 3D data.
  • A. Heale and L. Kleeman, "Fast target classification using sonar" IEEE/RSJ International Conference on Intelligent Robots and Systems, Hawaii, USA October 2001, p 1446-1451.
  • G. Taylor and L. Kleeman, "Flexible self-calibrated visual servoing for a humanoid robot" Proceedings of the Australian Conference on Robotics and Austomation 2001, Sydney November 2001. pp 79-84.
  • A. Heale and L. Kleeman, "A real time DSP sonar echo processor", IEEE/RSJ International Conference on Intelligent Robots and Systems, Takamatsu, Japan, October 2000, pp 1261-1266. Conference presentation here.
  • A. Heale and L. Kleeman, "A Sonar Sensor with Random Double Pulse Coding", Australian Conference on Robotics and Automation, Melbourne, August 30 - September 1, 2000, pp 81-86.
  • R. A. Russell, L. Kleeman, S. Kennedy "Using volatile chemicals to help locate targets in complex environments", Australian Conference on Robotics and Automation, Melbourne, August 30 - September 1, 2000, pp 87-92.
  • A. Price, G. Taylor and L. Kleeman, "Fast, robust colour vision for the monash humanoid", Australian Conference on Robotics and Automation, Melbourne, August 30 - September 1, 2000, pp 141-146.
  • L. Kleeman, "Fast and accurate sonar trackers using double pulse coding", IEEE/RSJ International Conference on Intelligent Robots and Systems, Kyongju, Korea, October 1999, pp.1185-1190. (winner of the Nakamura best paper award IROS'99).
  • K S Chong and L. Kleeman “Large Scale Sonarray Mapping using Multiple Connected Local Maps”, International Conference on Field and Service Robotics, ANU December 8-10, 1997, pp. 538-545.
  • K S Chong and L Kleeman, "Accurate odometry and error modelling for a mobile robot", IEEE International Conference on Robotics and Automation, Albuquerque USA, April 1997, pp. 2783-2788.
  • L Kleeman, "Scanned monocular sonar and the doorway problem", IEEE/RSJ International Conference on Intelligent Robots and Systems, Osaka, November 1996, pp 96-103.
  • H. Akbarally and L. Kleeman, "3D robot sensing from sonar and vision", IEEE International Conference on Robotics and Automation 1996, Minneapolis, Minnesota, April 1996 pp. 686-691.
  • H. Akbarally and L. Kleeman, "A sonar sensor for accurate 3D target localisation and classification", IEEE International Conference on Robotics and Automation 1995, Nagoya, Japan, May 1995 pp. 3003-3008.
  • L.Kleeman, "Optimal estimation of position and heading for mobile robots using ultrasonic beacons and dead-reckoning", IEEE International Conference on Robotics and Automation, Nice, France, pp 2582-2587, May 10-15 1992.
  • L. Kleeman, "Understanding and applying Kalman filtering", Proceedings of the Second Workshop on"Perceptive Systems" Jan 25, 26 1996, Curtin University of Technology, Perth Western Australia
  • C.Y.Chung and L.Kleeman, "Metastable-robust self-timed circuit synthesis from live safe simple signal transition graphs", Proceedings of the International Symposium on Advanced Research in Asynchronous Circuits and Systems, Salt Lake City, Utah USA Nov 3-5 1994, pp 97-105.
  • L.Kleeman, "Iterative algorithm for three dimensional autonomous robot localisation", Third National Conference on Robotics, Melbourne pp. 210 - 219 June 1990.
  • L.Kleeman, "Ultrasonic autonomous robot localisation system", IEEE international conference Intelligent Robots and Systems '89 Tsukuba, Japan, pp.212-219 September 1989.
  • L.Kleeman and A.Cantoni, "The modelling and performance analysis of batching arbiters", Joint Performance '86 and ACM SIGMETRICS 1986 Conference, North Carolina State University, USA pp 35-43, May 1986.

Well Cited Papers:

For current citations see Google Scholar Profile Lindsay Kleeman

Books

  • Geoffrey Taylor and Lindsay Kleeman, Visual Perception and Robotic Manipulation: 3D Object Recognition, Tracking and Hand-eye Coordination, Springer Tracts in Advanced Robotics (STAR), Vol 26 2006, ISBN: 3-540-33454-8, 218 pages + CD-ROM, buy it

  • L.Kleeman and R. Kuc, Chapter 21: “Sonar Sensing” in Springer Handbook of Robotics, Editors Bruno Siciliano and Oussame Khatib, ISBN 978-3-540-23957-4, Springer-Verlag Berlin Heidelberg 2008, pp 491-519.  This handbook won two Professional and Scholarly Excellence (The PROSE Awards) awards in 2008 from the Professional and Scholarly Publishing (PSP) Division of the Association of American Publishers (AAP).

Software

Videos:

  • DSP sonar tracking a moving plane: MPEGS: Low resolution (1 Mbyte) Medium (4 Mbyte)
  • Two DSP sonar sensors tracking the same wall on a moving robot - demonstration of interference rejection whilst classifying the wall as a plane. MPEGS: Low resolution (1 Mbyte) Medium (4 Mbyte)
  • Wiped sonar map building under joystick control. MPEGS: Low resolution (1 Mbyte) Medium (4 Mbyte)
  • Autonomous exploration and sonar mapping. MPEGS: Low resolution (1 Mbyte) Medium (4 Mbyte)
  • SLAM evolution of map building from sonar and laser: GIF: short (208 kbytes), long zoomed (2.2 Mbyte)
  • Interactive SLAM AVI: long (22 Mbytes) - for details see: A Diosi, G Taylor and L Kleeman, "Interactive SLAM using Laser and Advanced Sonar", Proceedings of the 2005 IEEE International Conference on Robotics and Automation Barcelona, Spain, April 2005, pp 1115-1120.
  • Robot manipulation and sensing videos from PhD student Geoff Taylor here

Online Theses:

Sonar SLAM (Simultaneous Localisation and Mapping) Map with Loops (jpg):

  • Map built using a robot running SLAM Extended Kalman Filtering from on-the-fly front and back sonar measurements during a wiping action. Yellow are features classified as a plane by the sonar, blue corners and green edges. Map with all measurements on 1 metre grid, final feature map. The robot path and error ellipses are shown in blue.

Seminars in powerpoint format:

Technical Reports Frequently Requested:

K S Chong and L. Kleeman "Precise odometry and statistical error modelling for a mobile robot", Technical report MECSE-96-6, Department of Electrical and Computer Systems Engg., Monash University 1996. Here is the conference presentation.

L. Kleeman "Odometry Error Covariance Estimation for Two Wheel Robot Vehicles", Technical report MECSE-95-1, Department of Electrical and Computer Systems Engg., Monash University 1995.

Other technical reports are available here: http://www.ds.eng.monash.edu.au/techrep/reports/

Archive of Zipped Postcript Papers:

Abstracts and Papers in Zipped Postscript for Download

  • Chong, K.S. and Kleeman, L. "Indoor Exploration using a Sonar Sensor Array: A Dual Representation Strategy", Proceedings 1997 IEEE/RSJ International Conference on Intelligent Robots and Systems.


    This paper presents an environmental acquisition strategy for a mobile robot using an advanced sonar sensor to achieve mapping navigation in an a priori unknown, imperfectly structured indoor environment. Most existing feature based strategies rely on unrealistic assumptions about the environment, while their grid based counterparts hinder localisation which leads to rapid degradation of map quality. A dual representation strategy is proposed here which exploits the strength of both a feature map and a grid map. With the advanced sensor, the environment is scanned and the obtained features are classified into planes, corners, edges and unknowns. The feature map is only updated with the first three types of features. Being sharper and more realistic than other representations such as uncertainty/bayesian maps, continual localisation is made possible. The grid map is updated with all measurements, including the unknowns resulting from complicated objects, to enable obstacle avoidance. On the grid map, Distance Transform based exploratory path planning is implemented. Adaptation has been made so that an explore-local-first behaviour is exhibited. Processing efficiency is improved with a simple dynamic memory allocation scheme. The paths generated by the Distance Transform are validated with a new local path validator that accounts for the limitation of sonar perception.

  • Chong, K.S. and Kleeman, L. "Sonar Feature Map Building for a Mobile Robot", Proceedings 1997 IEEE International Conference on Robotics and Automation.


    This report/paper describes a mobile robot equipped with a sonar sensor array, Werrimbi, in a guided feature based map building task in an indoor environment. Common indoor landmarks such as planes, corners and edges are located and classified with a multiple transducer sensor array. Accurate odometry information is derived from a pair of narrow unloaded encoder wheels. Discrete sonar observations are incrementally merged into partial planes to produce a realistic representation of environment. Collinearity constraints among features are exploited to enhance state estimation. The map update utilises Julier-Uhlmann Kalman Filter (JUKF) which improves the accuracy of covariance propagation through nonlinear equations and eliminates the need to derive Jacobian matrices. Correlation among map features and robot location are explicitly represented. Partial planes are also used to eliminate phantom targets caused by sonar specular reflection.

  • Chong, K.S. and Kleeman, L. "Accurate Odometry and Error Modelling for a Mobile Robot", Proceedings 1997 IEEE International Conference on Robotics and Automation.


    This report/paper presents a low cost novel odometry design capable of achieving high accuracy dead-reckoning. It also develops a statistical error model for estimating position and orientation errors of a mobile robot using odometry. Previous work on propagating odometry error covariance relies on incrementally updating the covariance matrix in small time steps. The approach taken here sums the noise theoretically over the entire path length to produce simple closed form expressions, allowing efficient covariance matrix updating after the completion of path segments. Closed form error covariance matrix is developed for a general circular arc and two special cases: (I) straight line and (II) turning about the centre of axle of the robot. Other paths can be composed of short segments of constant curvature arcs without great loss of accuracy. The model assumes that wheel distance measurement errors are exclusively random zero mean white noise. Systematic errors due to wheel radius and wheel base measurement were first calibrated with UMBmark [BorFen94]. Experimental results show that, despite its low cost, our system's performance, with regard to dead- reckoning accuracy, is comparable to some of the best, award-winning vehicles around. The statistical error model, on the other hand, needs to be improved in light of new insights.

  • L Kleeman, "Scanned monocular sonar and the doorway problem", accepted to IEEE/RSJ International Conference on Intelligent Robots and Systems 1996. (recommended for best paper award by a referee)


    A sonar system is presented that relies on scanning a single ultrasonic transducer and measuring echo amplitude and arrival times. Bearing angles to targets are estimated far more accurately than the transducer beamwidth as obtained with conventional sonar rings based on the Polaroid ranging module. A Gaussian beam characteristic is fitted using least squares to the amplitudes of corresponding echoes in the scan to obtain an estimate of the bearing to specular targets. As an illustration of the information gain over conventional sonar rings, the sensor approach is used on a mobile robot to find, traverse and map doorways reliably and with minimal algorithmic effort. This is compared with other work that claims the problem is difficult to solve using a conventional sonar ring of 24 Polaroid ranging modules

  • H. Akbarally and L. Kleeman, "3D robot sensing from sonar and vision", accepted to IEEE International Conference on Robotics and Automation 1996, Minneapolis - recommended for best student paper prize by a referee. see PDF version above.


    We describe a sensor that fuses sonar and visual data to create a three dimensional (3D) model of the environment with application to robot navigation. The environment is characterized by a set of connected horizontal and vertical lines. 3D sonar data is augmented by making deductions concerning the connection and definition of lines in 2D visual data. Any errors that may result from incorrect interpretation of the 2D camera data, such as false connections between lines, can be detected by moving the robot. Experimental results from the sensor are presented.

  • L. Kleeman and R. Kuc, "Mobile robot sonar for target localization and classification", International Journal of Robotics Research, Volume 14, Number 4, August 1995, pp 295-318.


    A novel sonar array is presented that has applications in mobile robotics for localization and mapping of indoor environments. The ultrasonic sensor localizes and classifies multiple targets in two dimensions to ranges of up to 8 meters. By accounting for effects of temperature and humidity, the system is accurate to within a millimeter and 0.1 degrees in still air. Targets separated by 10 mm in range can be discriminated. The error covariance matrix for these measurements is derived to allow fusion with other sensors. Targets are statistically classified into four reflector types: planes, corners, edges and unknown. The paper establishes that two transmitters and two receivers are necessary and sufficient to distinguish planes, corners and edges. A sensor array is presented with this minimum number of transmitters and receivers. A novel design approach is that the receivers are closely spaced so as to minimize the correspondence problem of associating different receiver echoes from multiple targets. A linear filter model for pulse transmission, reception, air absorption and dispersion is used to generate a set of templates for the echo as a function of range and bearing angle. The optimal echo arrival time is estimated from the maximum cross-correlation of the echo with the templates. The use of templates also allows overlapping echoes and disturbances to be rejected. Noise characteristics are modeled for use in the maximum likelihood estimates of target range and bearing. Experimental results are presented to verify assumptions and characterize the sensor.

  • C. Y. Chung and L. Kleeman, "Avoiding hazards in self-timed digital circuits derived from signal transition graphs", Australian Telecommunications Review, Vol. 29, No. 1, pp. 25-38, 1995.


    Since the introduction of Signal Transition Graphs (STGs) in the mid 1980s [1, 2], a number of techniques for the synthesis of self-timed circuits using STGs have been proposed. To achieve a hazard-free implementation, restrictions on the structure of the STG have been employed. Also, hazard-free design techniques have been incorporated into the synthesis procedure. Despite these, implementations derived using these techniques are not always hazard-free. Hazards are shown in this paper to be intrinsic to the function being implemented and cannot be eliminated. To avoid these hazards, certain timing conditions must be preserved. Previous attempts [3, 4, 5] to eliminate hazards are shown to have important limitations. A new procedure is proposed in this paper for the detection of hazards and timing constraints to avoid these hazards. The procedure is compared with previous attempts at hazard detection, and examples presented to show the limitations of other approaches.

  • L. Kleeman, "A three dimensional localiser for autonomous robot vehicles", Robotica, Vol 13, No 1 pp 87-94, 1995.


    A novel design of a three dimensional localiser intended for autonomous robot vehicles is presented. A prototype is implemented in air using ultrasonic beacons at known positions, and can be adapted to underwater environments where it has important applications, such as deep sea maintenance, data collection and reconnaissance tasks. The paper presents the hardware design, algorithms for position and orientation determination (six degrees of freedom), and performance results of a laboratory prototype. Two approaches are discussed for position and orientation determination - (i) fast single measurement set techniques and (ii) computationally slower Kalman filter based techniques. The Kalman filter approach allows the incorporation of robot motion information, more accurate beacon modelling and the capability of processing data from more than four beacons, the minimum number required for localisation.

  • H. Akbarally and L. Kleeman, "Sensor data fusion of sonar and visual data", Australian Robot Association conference "Robots for Australian Industries" Melbourne July 1995, pp. 288-305.


    In this paper we describe a technique that fuses sonar and visual data to create a three dimensional (3D) environmental model intended for robotic navigation. The model characterizes the environment as a set of connected horizontal and vertical lines. Starting with a measurement cycle from a new 3D sonar sensor, the environmental model is expanded successively to include lines from a camera view. The 3D sonar data is augmented by making deductions concerning the connection and definition of lines in the 2D visual data. Any errors that may result from incorrect interpretation of the 2D camera data, such as false connections between lines, can be detected by moving the robot to a second location. We illustrate the performance of this system by presenting experimental results from sensing a 3D structure.

  • H. Akbarally and L. Kleeman, "A sonar sensor for accurate 3D target localisation and classification", IEEE International Conference on Robotics and Automation 1995, Nagoya, Japan, May 1995 pp. 3003-3008. See PDF version above.


    This paper presents a novel sonar sensor consisting of three transmitters and three receivers that can localise and classify 3D targets into 16 different naturally occurring indoor classes. The sensor produces sub-millimeter range and sub-degree bearing accuracies using an optimal matched filter time of flight estimator up to a range of 6 meters. The sensor configuration, hardware and processing are described. Experimental results from the sensor are presented.

  • M.L.Hong and L. Kleeman, "A low sample rate 3D sonar sensor for mobile robots", IEEE International Conference on Robotics and Automation 1995, Nagoya, Japan, May 1995 pp. 3015-3020.


    This paper describes an ultrasonic sensor which uses the times of flight from three Polaroid ultrasonic transducers arranged in an equilateral triangle to identify and localise planes, 2D and 3D corners. The sensor employs a Maximum Likelihood Estimator and a data acquisition system with a low sampling rate of about 59kHz. The hardware and processing requirements are modest and fast due to the simple identification algorithms and sensor structure. Localisation of the objects can be achieved with range error of about 2mm and bearing error of less than 1°. The sensor has been applied to localising a robot in a known indoor environment using 3D natural features and has achieved accuracies of 1cm in position and 2° in bearing.

  • L.Kleeman and R.Kuc, "An optimal sonar array for target localization and classification", IEEE International Conference on Robotics and Automation, San Diego USA, May 1994 pp 3130-3135.


    A novel sonar array for mobile robots is presented with applications to localization and mapping of indoor environments. The ultrasonic sensor localizes and classifies multiple targets in two dimensions to ranges of up to 8 meters. By accounting for effects of temperature and humidity, the system is accurate to within 1 mm and 0.1 degrees in still air. Targets separated by 10 mm can be discriminated. Targets are classified into planes, corners, edges and unknown, with the minimum of two transmitters and two receivers. A novel approach is that receivers are closely spaced to minimize the correspondence problem of associating echoes from multiple targets. A set of templates is generated for echoes to allow the optimal arrival time to be estimated, and overlapping echoes and disturbances to be rejected.

  • C. Y. Chung and L. Kleeman, "An optimal approach to implementing self-timed logic circuits from signal transition graphs", Australian Telecommunications Review, Vol 27, No. 2, pp. 41-56, 1993.


    Scaling of integrated circuits in recent years has resulted in improvements in speed and density of VLSI circuits. However scaling has also aggravated clock skew problems due to increasing wire delays. Consequently, to exploit the speed improvements of scaling and to avoid synchronisation failure in synchronous systems, designers are now turning towards self-timed system design for solutions. Amongst the techniques for the synthesis of self-timed circuits, an approach using Signal Transition Graphs was introduced by Chu [1, 2, 3]. In an attempt to simplify his method and to achieve efficient results, he uses a procedure called net contraction to decompose a Signal Transition Graph into simpler subgraphs known as contracted STGs. With the possibility of state assignment problems in the contracted STGs, net contraction introduces complications and inefficiencies. The cause of these deficiencies can be shown to be the criterion upon which a signal is retained during net contraction. To avoid these deficiencies, a new approach is presented that derives circuit implementations from uncontracted state graphs using Quine-McCluskey tabular Karnaugh mapping and Prime Implicant tables. This approach is shown to produce hazard free implementations with a minimum number of gates in contrast to Chu's sub-optimal method.

  • L.Kleeman, "Optimal estimation of position and heading for mobile robots using ultrasonic beacons and dead-reckoning", IEEE International Conference on Robotics and Automation, Nice, France, pp 2582-2587, May 10-15 1992.


    An active beacon localisation system is described that estimates position and heading for a mobile robot. An Iterated Extended Kalman Filter is applied to the beacon and dead-reckoning data to estimate optimal values of position and heading, given a model for the localiser and robot motion. This paper describes the implementation and experimental results of the localisation system. Position and heading angle updates are calculated in real time every 150 milliseconds with a measured standard deviation of path error of 40 mm in a 12 metre square workspace.

Hockey

1978-1983 Newcastle Uni and intervarsity rep.  

2007 onwards with Doncaster hockey club. Best and fairest Vet C 2008 scoring ~1 goal per match.  

Highlights Doncaster versus Kew 2009.

Department of Electrical and Computer Systems Engineering | Faculty of Engineering | Monash University
Last updated : 2016