Mark Sheehan

/Mark Sheehan

MRG Publications

2013

  • [PDF] 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,
    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.},
    Address = {Tokyo, Japan},
    Author = {Mark Sheehan and Alastair Harrison and Paul Newman},
    Booktitle = {Proc. {IEEE}/{RSJ} International Conference on Intelligent Robots and Systems (IROS2013)},
    Date-Added = {2013-11-06 11:44:19 +0000},
    Date-Modified = {2013-11-06 11:51:32 +0000},
    Month = {November},
    Pdf = {http://www.robots.ox.ac.uk/~mobile/Papers/2013IROS_sheehan.pdf},
    Title = {Continuous Vehicle Localisation Using Sparse 3D Sensing, Kernelised Renyi Distance and Fast Gauss Transforms},
    Year = {2013}}

2012

  • [PDF] M. Sheehan, A. Harrison, and P. Newman, “Self-calibration for a 3D laser,” The International Journal of Robotics Research, 2012.
    [Bibtex]
    @article{SheehanCalibration2012,
    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.},
    Author = {Mark Sheehan and Alastair Harrison and Paul Newman},
    Journal = {The International Journal of Robotics Research},
    Keywords = {3D Laser Calibration},
    Owner = {mcs},
    Pdf = {http://www.robots.ox.ac.uk/~mobile/Papers/2012IJR_mark.pdf},
    Title = {Self-calibration for a 3D laser},
    Url = {http://ijr.sagepub.com/content/early/2011/12/21/0278364911429475},
    Year = {2012},
    Bdsk-Url-1 = {http://ijr.sagepub.com/content/early/2011/12/21/0278364911429475}}

2010

  • [PDF] 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,
    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.},
    Address = {New Delhi and Agra, India},
    Author = {Mark Sheehan and Alastair Harrison and Paul Newman},
    Booktitle = {In Proceedings of the International Symposium on Experimental Robotics (ISER2010)},
    Keywords = {3D Laser Calibration},
    Month = {December},
    Note = {12},
    Pdf = {http://www.robots.ox.ac.uk/~mobile/Papers/SheehanHarrisonNewman_ISER2010.pdf},
    Title = {Automatic Self-Calibration Of A Full Field-Of-View 3D n-Laser Scanner},
    Year = {2010}}
2014-12-09T13:47:40+00:00