9 research outputs found

    Model based Detection and 3D Localization of Planar Objects for Industrial Setups

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    In this work we present a method to detect and estimate the three-dimensional pose of planar and textureless objects placed randomly on a conveyor belt or inside a bin. The method is based on analysis of single 2D images acquired by a standard camera. The algorithm exploits a template matching method to recognize the objects. A set of pose hypotheses are then refined and, based on a gradient orientation scoring, the best object to be manipulated is selected. The method is flexible and can be used with different objects without changing parameters since it exploits a CAD model as input for template generation. We validated the method using synthetic images. An experimental setup has been also designed using a fixed standard camera to localize planar metal objects in various scenarios

    A Graph-based Optimization Framework for Hand-Eye Calibration for Multi-Camera Setups

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    Hand-eye calibration is the problem of estimating the spatial transformation between a reference frame, usually the base of a robot arm or its gripper, and the reference frame of one or multiple cameras. Generally, this calibration is solved as a non-linear optimization problem, what instead is rarely done is to exploit the underlying graph structure of the problem itself. Actually, the problem of hand-eye calibration can be seen as an instance of the Simultaneous Localization and Mapping (SLAM) problem. Inspired by this fact, in this work we present a pose-graph approach to the hand-eye calibration problem that extends a recent state-of-the-art solution in two different ways: i) by formulating the solution to eye-on-base setups with one camera; ii) by covering multi-camera robotic setups. The proposed approach has been validated in simulation against standard hand-eye calibration methods. Moreover, a real application is shown. In both scenarios, the proposed approach overcomes all alternative methods. We release with this paper an open-source implementation of our graph-based optimization framework for multi-camera setups.Comment: This paper has been accepted for publication at the 2023 IEEE International Conference on Robotics and Automation (ICRA

    From Constraints to Opportunities: Efficient Object Detection Learning for Humanoid Robots

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    Reliable perception and efficient adaptation to novel conditions are priority skills for robots that function in ever-changing environments. Indeed, autonomously operating in real world scenarios raises the need of identifying different context\u2019s states and act accordingly. Moreover, the requested tasks might not be known a-priori, requiring the system to update on-line. Robotic platforms allow to gather various types of perceptual information due to the multiple sensory modalities they are provided with. Nonetheless, latest results in computer vision motivate a particular interest in visual perception. Specifically, in this thesis, I mainly focused on the object detection task since it can be at the basis of more sophisticated capabilities. The vast advancements in latest computer vision research, brought by deep learning methods, are appealing in a robotic setting. However, their adoption in applied domains is not straightforward since adapting them to new tasks is strongly demanding in terms of annotated data, optimization time and computational resources. These requirements do not generally meet current robotics constraints. Nevertheless, robotic platforms and especially humanoids present opportunities that can be exploited. The sensors they are provided with represent precious sources of additional information. Moreover, their embodiment in the workspace and their motion capabilities allow for a natural interaction with the environment. Motivated by these considerations, in this Ph.D project, I mainly aimed at devising and developing solutions able to integrate the worlds of computer vision and robotics, by focusing on the task of object detection. Specifically, I dedicated a large amount of effort in alleviating state-of-the-art methods requirements in terms of annotated data and training time, preserving their accuracy by exploiting robotics opportunity
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