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The aim of this ROS package is to provide detection and pose estimation of an H-shaped helipad for the purpose of landing a UAV. The approach is based on this paper. It provides robust and accurate detection of the helipad from different angles and orientations.
Given Below is the image of the Helipad.
We obtain a closed contour of the H, given by a parameterized function , where
and each
represents a point of the contour on the image. We define the size of the contour to be it's Cardinality
.
Define a function , where
would give us the 'Sharpness' around the particular point of the contour. This can be achieved by considering the distance of a particular point
to the line connecting
and
. This distance will be close to 0 for points along relatively straight lines and give a maxima around corners. So,
is the required distance for each point on the contour. This gives us a 'signature' of that particular contour.
Given below is the representation of for
on a straight line not near a corner followed by the case where it is near a corner.
The Signature of an experimentally detected H.
We compare this signature with the signature of the required H by checking the ratio of the distance between the maximas and their contour size in their individual signatures. For the centre of the H, we use the centroid of all the corners obtained by taking the average of where
is a point of maxima of
for all
.
The raw image obtained from the subscribed image stream is converted to the grayscale colorspace. A Gaussian blur and adaptive thresholding is applied to the image so as to accentuate the H-shaped marker. An opening filter is then applied to it.The contours of this image are then generated. These contours are analyzed with the PointToLineDistance function and stored in a vector. A smoothing operation is applied to the distance data and a corresponding signature is made. This signature is compared to an ideal H signature, allowing some degree of tolerance. The center of the H is computed in the image frame.