Self-aware robots rely on depth sensing to interact with the surrounding environment, e.g. to pursue object grasping. Yet, dealing with tiny items, often occurring in industrial robotics scenarios, may represent a challenge due to lack of sensors yielding sufficiently accurate depth measurements. Existing active sensors fail at measuring details of small objects (< 1cm) because of limitations in the working range, e.g. usually beyond 50 cm away, while off-the-shelf stereo cameras are not suited to close-range acquisitions due to the need for extremely short baselines. Therefore, we propose a framework designed for accurate depth sensing and particularly amenable to reconstruction of miniature objects. By leveraging on a single camera mounted in eye-on-hand configuration and the high repeatability of a robot, we acquire multiple images and process them through a stereo algorithm revised to fully exploit multiple vantage points. Using a novel dataset addressing performance evaluation in industrial applications, our Single camera Stereo Robot (SiSteR) delivers high accuracy even when dealing with miniature objects. We will provide a public dataset and an open-source implementation of our proposal to foster further development in this field.
De Gregorio D., Poggi M., Zama Ramirez P., Palli G., Mattoccia S., Di Stefano L. (2021). Beyond the Baseline: 3D Reconstruction of Tiny Objects with Single Camera Stereo Robot. IEEE ACCESS, 9, 119755-119765 [10.1109/ACCESS.2021.3108626].
Beyond the Baseline: 3D Reconstruction of Tiny Objects with Single Camera Stereo Robot
De Gregorio D.
;Poggi M.;Zama Ramirez P.;Palli G.;Mattoccia S.;Di Stefano L.
2021
Abstract
Self-aware robots rely on depth sensing to interact with the surrounding environment, e.g. to pursue object grasping. Yet, dealing with tiny items, often occurring in industrial robotics scenarios, may represent a challenge due to lack of sensors yielding sufficiently accurate depth measurements. Existing active sensors fail at measuring details of small objects (< 1cm) because of limitations in the working range, e.g. usually beyond 50 cm away, while off-the-shelf stereo cameras are not suited to close-range acquisitions due to the need for extremely short baselines. Therefore, we propose a framework designed for accurate depth sensing and particularly amenable to reconstruction of miniature objects. By leveraging on a single camera mounted in eye-on-hand configuration and the high repeatability of a robot, we acquire multiple images and process them through a stereo algorithm revised to fully exploit multiple vantage points. Using a novel dataset addressing performance evaluation in industrial applications, our Single camera Stereo Robot (SiSteR) delivers high accuracy even when dealing with miniature objects. We will provide a public dataset and an open-source implementation of our proposal to foster further development in this field.File | Dimensione | Formato | |
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Beyond_the_Baseline_3D_Reconstruction_of_Tiny_Objects_With_Single_Camera_Stereo_Robot.pdf
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