Enhancing Object Detection Performance Through Sensor Pose Definition with Bayesian Optimization

Abstract

Robots equipped with vision systems at the end-effector provide a powerful combination in industrial contexts. While much attention is dedicated to machine vision algorithms, the optimization of the vision system pose is not properly addressed (to increase object detection performance). A complete pipeline for such optimization is proposed. To this aim, Bayesian Optimization is employed. A Franka EMIKA Panda robot has been used as a robotic platform, equipped at its end-effector with an Intel© RealSense D400. Achieved results show the high-fidelity reconstruction of the real working environment for the offline optimization (i.e., performed simulations), together with capabilities of the proposed Bayesian Optimization-based approach to defining the sensor pose in a limited number of experimental trials (50 maximum iterations has been considered).