Parallel implementation of RAndom SAmple Consensus (RANSAC) Adarsh Kowdle

Algorithm description Iterative method to estimate parameters of a mathematical model from a set of observed data, which contains outliers A simple form of RANSAC considered for the project

Algorithm description Step 1: Randomly sample the data to obtain two points Step 2: Determine the parameters of the line joining these two points Step 3: Evaluate the distance of every other points from this line => serves as error function to be minimized Step 4: Repeat Step 1 till the required number of iterations have been completed Step 5: The resulting line parameters represents the best line fit for the given observations of 2D points.

Parallel design proposed - OpenMP Master Thread 1 Thread 2 Thread 3Thread N Suppose that there are data points Master randomly samples data points and passes it to the threads Thread 1 Thread 2 Thread 3Thread N Data points in shared memory, instantiate threads to compute error Different variants of the code tried out

Results and conclusions Extracted data points offline using Matlab – about data points The data points were created by adding noise to it a known line, so, best fit line is known Implemented the parallel version of RANSAC using OpenMP directives and tested with these data points Performance – Serial code took 0.24 seconds for 5000 iterations – OpenMP parallel implementation takes 0.05 seconds for 5000 iterations

Results and conclusions A parallel version of RANSAC has been implemented With the use of OMP directives, a five-fold decrease in time taken has been achieved which is significant