implementation of a Python function that implements the RANSAC algorithm to perform plane fitting. Plane fitting...

  

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implementation of a Python function that implements the RANSAC algorithm to perform plane fitting. Plane fitting is the process of finding the best-fit plane that passes through a set of points. RANSAC (Random Sample Consensus) is a popular algorithm used for robust plane fitting in the presence of outliers. The RANSAC function will take the following inputs: 1. points: A 3D numpy array containing the x, y, and coordinates of the points to fit a plane to. 2. num-iterations: The number of iterations to run the RANSAC algorithm. 3. distance-threshold: The maximum distance between a point and the plane for it to be consid- ered an inlier. 4. min-inliers: The minimum number of inliers required for a plane to be considered a good fit. Inside the function, you will: - Randomly sample 3 points from the input data. Fit a plane to the sample using the least square fit method. - Calculate the distance between each point and the plane. Count the number of inliers. Update the best plane if this iteration produced a better fit. Return the best-fit plane. . - - - Tasks (a) Using Python, load and plot the given 3D point cloud. implementation of a Python function that implements the RANSAC algorithm to perform plane fitting. Plane fitting is the process of finding the best-fit plane that passes through a set of points. RANSAC (Random Sample Consensus) is a popular algorithm used for robust plane fitting in the presence of outliers. The RANSAC function will take the following inputs: 1. points: A 3D numpy array containing the x, y, and coordinates of the points to fit a plane to. 2. num-iterations: The number of iterations to run the RANSAC algorithm. 3. distance-threshold: The maximum distance between a point and the plane for it to be consid- ered an inlier. 4. min-inliers: The minimum number of inliers required for a plane to be considered a good fit. Inside the function, you will: - Randomly sample 3 points from the input data. Fit a plane to the sample using the least square fit method. - Calculate the distance between each point and the plane. Count the number of inliers. Update the best plane if this iteration produced a better fit. Return the best-fit plane. . - - - Tasks (a) Using Python, load and plot the given 3D point cloud.

Answer rating: 100% (QA)

a Loading and plotting the 3D point cloud First we need to import the required libraries and load the data from the given file We can use the matplotlib library to plot the 3D point cloud import numpy ... View the full answer