Mark Sheehan

MRG Publications

2013

• M. Sheehan, A. Harrison, and P. Newman, “Continuous Vehicle Localisation Using Sparse 3D Sensing, Kernelised Renyi Distance and Fast Gauss Transforms,” in Proc. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS2013), Tokyo, Japan, 2013.
  [Bibtex]

  @InProceedings{Sheehan:IROS2013,
  author = {Mark Sheehan and Alastair Harrison and Paul Newman},
  title = {Continuous Vehicle Localisation Using Sparse 3D Sensing, Kernelised Renyi Distance and Fast Gauss Transforms},
  booktitle = {Proc. {IEEE}/{RSJ} International Conference on Intelligent Robots and Systems (IROS2013)},
  year = {2013},
  address = {Tokyo, Japan},
  month = {November},
  abstract = {This paper is about estimating a smooth, continuous-time trajectory of a vehicle relative to a prior 3D laser map. We pose the estimation problem as that of finding a sequence of Catmull-Rom splines which optimise the Kernelised R{\'e}nyi Distance (KRD) between the prior map and live measurements from a 3D laser sensor. Our approach treats the laser measurements as a continual stream of data from a smoothly moving vehicle. We side-step entirely the segmentation and feature matching problems incumbent in traditional point cloud matching algorithms, relying instead on a smooth and well behaved objective function. Importantly our approach admits the exploitation of sensors with modest sampling rates - sensors that take seconds to densely sample the workspace. We show how by appropriate use of the Improved Fast Gauss Transform we can reduce the order of the estimation problem from quadratic (straight forward application of the KRD) to linear. Although in this paper we use 3D laser, our approach is also applicable to vehicles using 2D laser sensing or dense stereo. We demonstrate and evaluate the performance of our approach when estimating the full 6DOF continuous time pose of a road vehicle undertaking over 2.7km of outdoor travel.},
  date-added = {2013-11-06 11:44:19 +0000},
  date-modified = {2013-11-06 11:51:32 +0000},
  pdf = {http://www.robots.ox.ac.uk/~mobile/Papers/2013IROS_sheehan.pdf},
  }

2012

• M. Sheehan, A. Harrison, and P. Newman, “Self-calibration for a 3D laser,” The International Journal of Robotics Research, 2012.
  [Bibtex]

  @Article{SheehanCalibration2012,
  author = {Mark Sheehan and Alastair Harrison and Paul Newman},
  title = {Self-calibration for a 3D laser},
  journal = {The International Journal of Robotics Research},
  year = {2012},
  abstract = {In this paper we describe a method for the automatic self-calibration
  of a 3D laser sensor. We wish to acquire crisp point clouds and so
  we adopt a measure of crispness to capture point cloud quality. We
  then pose the calibration problem as the task of maximizing point
  cloud quality. Concretely, we use Rnyi Quadratic Entropy to measure
  the degree of organization of a point cloud. By expressing this quantity
  as a function of key unknown system parameters, we are able to deduce
  a full calibration of the sensor via an online optimization. Beyond
  details on the sensor design itself, we fully describe the end-to-end
  intrinsic parameter calibration process and the estimation of the
  clock skews between the constituent microprocessors. We analyse performance
  using real and simulated data and demonstrate robust performance
  over 30 test sites.},
  bdsk-url-1 = {http://ijr.sagepub.com/content/early/2011/12/21/0278364911429475},
  pdf = {2012IJR_mark.pdf:http\://www.robots.ox.ac.uk/~mobile/Papers/2012IJR_mark.pdf},
  keywords = {3D Laser Calibration},
  owner = {mcs},
  url = {http://ijr.sagepub.com/content/early/2011/12/21/0278364911429475},
  }

2010

• M. Sheehan, A. Harrison, and P. Newman, “Automatic Self-Calibration Of A Full Field-Of-View 3D n-Laser Scanner,” in In Proceedings of the International Symposium on Experimental Robotics (ISER2010), New Delhi and Agra, India, 2010.
  [Bibtex]

  @InProceedings{Sheehan2010,
  author = {Mark Sheehan and Alastair Harrison and Paul Newman},
  title = {Automatic Self-Calibration Of A Full Field-Of-View 3D n-Laser Scanner},
  booktitle = {In Proceedings of the International Symposium on Experimental Robotics (ISER2010)},
  year = {2010},
  address = {New Delhi and Agra, India},
  month = {December},
  note = {12},
  abstract = {This paper describes the design, build, automatic self-calibration
  and evaluation of a 3D Laser sensor using conventional parts. Our
  goal is to design a system, which is an order of magnitude cheaper
  than commercial systems, with commensurate performance. In this paper
  we adopt point cloud "crispness" as the measure of system performance
  that we wish to optimise. Concretely, we apply the information theoretic
  measure known as R{\`E}nyi Quadratic Entropy to capture the degree of
  organisation of a point cloud. By expressing this quantity as a function
  of key unknown system parameters, we are able to deduce a full calibration
  of the sensor via an online optimisation. Beyond details on the sensor
  design itself, we fully describe the end-to-end extrinsic parameter
  calibration process, the estimation of the clock skews between the
  four constituent microprocessors and analyse the effect our spatial
  and temporal calibrations have on point cloud quality.},
  pdf = {http://www.robots.ox.ac.uk/~mobile/Papers/SheehanHarrisonNewman_ISER2010.pdf},
  keywords = {3D Laser Calibration},
  }