In this work, we are concerned with planning paths from overhead imagery. The novelty here lies in taking explicit account of uncertainty in terrain classification and spatial variation in terrain cost. The image is first classified using a multi-class Gaussian Process Classifier which provides probabilities of class membership at each location in the image, which is then combined with a terrain cost evaluated at that location using a spatial Gaussian process. The resulting cost function is, in turn, passed to a planner. This allows both the uncertainty in terrain classification and spatial variations in terrain costs to be incorporated into the planned path. Because the cost of traversing a grid cell is now a probability density rather than a single scalar value, we can produce not only the most-likely shortest path between points on the map, but also sample from the cost map to produce a distribution of paths between the points

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  • [PDF] L. Murphy and P. Newman, “Planning Most-Likely Paths from Overhead Imagery,” in Proc. IEEE International Conference on Robotics and Automation (ICRA’10), Anchorage, AK, 2010.
    [Bibtex]

    @inproceedings{Murphy2010,
    Address = {Anchorage, AK},
    Author = {Liz Murphy and Paul Newman},
    Booktitle = {Proc. {IEEE} International Conference on Robotics and Automation (ICRA'10)},
    Keywords = {Planning},
    Month = {May},
    Note = {05},
    Pdf = {http://www.robots.ox.ac.uk/~mobile/Papers/MurphyICRA2010.pdf},
    Title = {Planning Most-Likely Paths from Overhead Imagery},
    Year = {2010}}