Parallel implementation of RAndom SAmple Consensus RANSAC Adarsh

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 2 D points.

Parallel design proposed - Open. MP Suppose that there are 10000 data points Thread 1 Master randomly samples data points and passes it to the threads Master Thread 2 Thread 3 Thread N Data points in shared memory, instantiate threads to compute error Thread 1 Thread 2 Thread 3 Different variants of the code tried out Thread N

Results and conclusions • Extracted data points offline using Matlab – about 10000 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 Open. MP directives and tested with these data points • Performance – Serial code took 0. 24 seconds for 5000 iterations – Open. MP 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