Quantitative performance optimisation for corner and edge based robotic vision systems: A Monte-Carlo simulation

Abstract

Corner and edge based robotic vision systems have achieved enormous success in various applications. To quantify and thereby improve the system performance, the standard method is to conduct cross comparisons using benchmark datasets. Such datasets, however, are usually generated for validating specific vision algorithms (e.g. monocular SLAM[1] and stereo odometry [2]). In addition, they are not capable of evaluating robotic systems which require visual feedback signals for motion control (e.g. visual servoing [3]). To develop a more generalised framework to evaluate ordinary corner and edge based robotic vision systems, we propose a novel Monte-Carlo simulation which contains various real-world geometric uncertainty sources. An edge-based global localisation algorithm is evaluated and optimised using the proposed simulation via a large scale Monte-Carlo analysis. During a long-term optimisation, the system performance is improved by around 230 times, while preserving high robustness towards all the simulated uncertainty sources.