In the field of robotics, one of the essential tasks for a robot to accomplish its goals is to know its own location. The localization problem is a complex task and, usually, different systems, methods and sensors are needed to achieve it. One of these methods is the iterative closest points (ICP) algorithm. The main problem that ICP presents is its high computational time. Due to this, it is common to filter its input data before calculating the final transformation. In this paper, a comparative study in terms of precision and execution time among the most popular filters used in combination with the ICP is presented. The study indicates that the scale-invariant feature transform (SIFT) is the filter that better improves the execution time, while its combination with the RANdom SAmple Consensus (RANSAC) obtains better precision results.
Comparative Study of the Execution Time of Parallel Heat Equation on CPU and GPU
