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70 lines
3.1 KiB
ReStructuredText
70 lines
3.1 KiB
ReStructuredText
Object shape recognition using rectangle fitting
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This is an object shape recognition using rectangle fitting.
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.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Mapping/rectangle_fitting/animation.gif
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This example code is based on this paper algorithm:
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- `Efficient L\-Shape Fitting for Vehicle Detection Using Laser Scanners \- The Robotics Institute Carnegie Mellon University <https://www.ri.cmu.edu/publications/efficient-l-shape-fitting-for-vehicle-detection-using-laser-scanners>`_
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The algorithm consists of 2 steps as below.
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Step1: Adaptive range segmentation
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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In the first step, all range data points are segmented into some clusters.
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We calculate the distance between each range data and the nearest range data, and if this distance is below a certain threshold, it is judged to be in the same cluster.
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This distance threshold is determined in proportion to the distance from the sensor.
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This is taking advantage of the general model of distance sensors, which tends to have sparser data distribution as the distance from the sensor increases.
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The threshold range is calculated by:
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.. math:: r_{th} = R_0 + R_d * r_{origin}
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where
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- :math:`r_{th}`: Threashold range
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- :math:`R_0, R_d`: Constant parameters
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- :math:`r_{origin}`: Distance from the sensor for a range data.
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Step2: Rectangle search
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~~~~~~~~~~~~~~~~~~~~~~~~~~
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In the second step, for each cluster calculated in the previous step, rectangular fittings will be applied.
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In this rectangular fitting, each cluster's distance data is rotated at certain angle intervals.
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It is evaluated by one of the three evaluation functions below, then best angle parameter one is selected as the rectangle shape.
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1. Rectangle Area Minimization criteria
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=========================================
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This evaluation function calculates the area of the smallest rectangle that includes all the points, derived from the difference between the maximum and minimum values on the x-y axis for all distance data points.
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This allows for fitting a rectangle in a direction that encompasses as much of the smallest rectangular shape as possible.
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2. Closeness criteria
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======================
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This evaluation function uses the distances between the top and bottom vertices on the right side of the rectangle and each point in the distance data as evaluation values.
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If there are points on the rectangle edges, this evaluation value decreases.
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3. Variance criteria
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=======================
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This evaluation function uses the squreed distances between the edges of the rectangle (horizontal and vertical) and each point.
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Calculating the squared error is the same as calculating the variance.
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The smaller this variance, the more it signifies that the points fit within the rectangle.
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API
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~~~~~~
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.. autoclass:: Mapping.rectangle_fitting.rectangle_fitting.LShapeFitting
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:members:
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References
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~~~~~~~~~~
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- `Efficient L\-Shape Fitting for Vehicle Detection Using Laser Scanners \- The Robotics Institute Carnegie Mellon University <https://www.ri.cmu.edu/publications/efficient-l-shape-fitting-for-vehicle-detection-using-laser-scanners>`_
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