A Parallel Implementation of Multiple Non-overlapping Cameras for Robot Pose Estimation

Abstract

Image processing and computer vision algorithms are gaining larger concern in a variety of application areas such as robotics and man-machine interaction.Vision allows the development of flexible, intelligent, and less intrusive approaches than most of the other sensor systems.In this work, we determine the location and orientation of a mobile robot which is crucial for performing its tasks.In order to be able to operate in real time there is a need to speed up different vision routines.Therefore, we present and evaluate a method for introducing parallelism into the multiple non-overlapping camera pose estimation algorithm proposed in [1].In this algorithm the problem has been solved in real time using multiple non-overlapping cameras and the Extended Kalman Filter (EKF).Four cameras arranged in two back-to-back pairs are put on the platform of a moving robot.An important benefit of using multiple cameras for robot pose estimation is the capability of resolving vision uncertainties such as the bas-relief ambiguity.The proposed method is based on algorithmic skeletons for low, medium and high levels of parallelization.The analysis shows that the use of a multiprocessor system enhances the system performance by about 87%.In addition, the proposed design is scalable, which is necaccery in this application where the number of features changes repeatedly.