CODEWITHSUNDEEP
image-processingcomputer-visionpoint-cloud-libraryroboticspoint-clouds
2017_John
2017_John

Reputation: 99

Calculation of center point for the localization of robot in 3D data

I am trying to find a reliable method to calculate the corner points of a container. From these corner point’s idea is to calculate the center point of the container for the localization of robot, it means that the calculated center point will be the destination of robot in order to pick the container. For this I am looking for any suggestions to calculate the corner points or may be if any possibility to calculate the center point directly. Up to this point PCL library C/C++ is used for the processing of the 3D data. The image below is the screenshot of the container.
thanks in advance.

afterApplyingPassthrough

Upvotes: 1

Views: 808

Answers (2)

Sneaky Polar Bear
Sneaky Polar Bear

Reputation: 1671

I assume the wheels maintain the cart a known offset from the floor and you can identify the floor. Filter out all points which are too close to the floor (this will remove wheels and everything but cart which will help limit data and simplify later steps.

If you isolate the cart, you could apply a simple average point (centroid), alternately, if that is not precise, you could try finding the bounding box of the isolated cart (min max in primary directions) and then take the centroid of that bounding box (this should be more accurate, but will still need a slight vertical offset due to the top handles).

If you can not isolate the cart or the other methods are not working well, you could try using PCL sample consensus specifically SACMODEL_LINE. This will be an involved strategy, but will give very solid results, basically run through and find each line and subtract its members from the cloud so as to find the next best line. After you have your 4 primary cart lines, use their parameters to find your centroid. *this would also be robust against random items being in or on the cart as well as carts of various sizes (assuming they always had linear perpendicular walls)

Upvotes: 1

Nejc
Nejc

Reputation: 937

I did the following things:

  • I binarized the image (black pixels = 0, green pixels = 1),
  • inverted the image (black pixels = 1, green pixels = 0),
  • eroded the image with 3x3 kernel N-times and dilated it with same kernel M-times.

Left: N=2, M=1;Right: N=6, M=6

enter image description here enter image description here

After that:

  • I computed contours of all non-zero areas and
  • removed the contour that surrounded entire image.

This are the contours that remained:

enter image description here enter image description here

I do not know how "typical" input image looks like in your case. Since I only have access to one sample image, I would rather not speculate about "general solution" that will be suitable for you. But to solve this particular case, you could analyze every contour in the following way:

  • compute rotatated rectangle that fits best around your contour (you need something similar to minAreaRect from OpenCV)
  • compute areas of rectangle and contour interior
  • if the difference between contour area and the area of the rotated bounding rectangle is small, the contour has approximately rectangular shape
  • find the contour that is both rectangular and satisfies some other condition (for example: typical area of the container). Assume that this belongs to container and compute its center.

I am not claiming that this is a solution that will work well in real world scenarios. It is also not fast. You should view it as a "sketch" that shows how to extract some useful information.

Upvotes: 2

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