Automatic dismantling integrating optical flow into a machine vision-controlled robot system

Gengenbach, V.; Nagel, Hans-Hellmut; Tonko, M.; Schäfer, K.

doi:10.1109/robot.1996.506889

Cited by 15 publications

(9 citation statements)

References 10 publications

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“…While the range of techniques proposed for these purposes is vast, not many of them provide fully automated systems; most of the approaches developed for these tasks rely on overly simple initialization steps [11], cumbersome manual pose initialization procedures [12], or do not consider the issue at all [9].…”

Section: Related Workmentioning

confidence: 99%

Object recognition and full pose registration from a single image for robotic manipulation

Collet

Berenson

Srinivasa

et al. 2009

2009 IEEE International Conference on Robotics and Automation

216

141

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Abstract-Robust perception is a vital capability for robotic manipulation in unstructured scenes. In this context, full pose estimation of relevant objects in a scene is a critical step towards the introduction of robots into household environments. In this paper, we present an approach for building metric 3D models of objects using local descriptors from several images. Each model is optimized to fit a set of calibrated training images, thus obtaining the best possible alignment between the 3D model and the real object. Given a new test image, we match the local descriptors to our stored models online, using a novel combination of the RANSAC and Mean Shift algorithms to register multiple instances of each object. A robust initialization step allows for arbitrary rotation, translation and scaling of objects in the test images. The resulting system provides markerless 6-DOF pose estimation for complex objects in cluttered scenes. We provide experimental results demonstrating orientation and translation accuracy, as well a physical implementation of the pose output being used by an autonomous robot to perform grasping in highly cluttered scenes.

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Section: Related Workmentioning

confidence: 99%

Object recognition and full pose registration from a single image for robotic manipulation

Collet

Berenson

Srinivasa

et al. 2009

2009 IEEE International Conference on Robotics and Automation

216

141

View full text Add to dashboard Cite

show abstract

“…Many of the reported systems use manual pose initialization where the user establishes the correspondence between the model and object features [10,14]. Although there are systems for which this step is performed automatically, [13,20,27] the proposed approaches are time consuming and not appealing for real-time applications.…”

Section: Related Workmentioning

confidence: 99%

“…As it can be seen, the pose of the target is used as measurement rather than image features, as commonly used in the literature, [9,13]. An approach similar to the one presented here was considered in Ref.…”

Section: Prediction and Updatementioning

confidence: 99%

Object recognition and pose estimation using color cooccurrence histograms and geometric modeling

Ekvall

Kragić

Hoffmann

2005

Image and Vision Computing

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“…Many similar systems use manual pose initialization where the correspondence between the model and object features is given by the user (Giordana et al 2000;Drummond and Cipolla 2000). Although there are systems where this step is performed automatically, the approaches are time-consuming and not appealing for real-time applications (Gengenbach et al 1996;Lowe 1985). One additional problem, in our case, is that the objects to be manipulated by the robot are highly textured (see Figure 9) and therefore not suited for matching approaches based on, for example, line features (Koller, Daniilidis, and Nagel 1993;Vincze, Ayromlou, and Kubinger 1999;.…”

Section: Model-based Visual Servoingmentioning

confidence: 99%

Robust Visual Servoing

Kragić

Christensen

2003

The International Journal of Robotics Research

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For service robots operating in domestic environments, it is not enough to consider only control level robustness; it is equally important to consider how image information that serves as input to the control process can be used so as to achieve robust and efficient control.In this paper we present an effort towards the development of robust visual techniques used to guide robots in various tasks. Given a task at hand, we argue that different levels of complexity should be considered; this also defines the choice of the visual technique used to provide the necessary feedback information. We concentrate on visual feedback estimation where we investigate both two-and three-dimensional techniques.In the former case, we are interested in providing coarse information about the object position/velocity in the image plane. In particular, a set of simple visual features (cues) is employed in an integrated framework where voting is used for fusing the responses from individual cues. The experimental evaluation shows the system performance for three different cases of camera-robot configurations most common for robotic systems.For cases where the robot is supposed to grasp the object, a twodimensional position estimate is often not enough. Complete pose (position and orientation) of the object may be required. Therefore, we present a model-based system where a wire-frame model of the object is used to estimate its pose. Since a number of similar systems have been proposed in the literature, we concentrate on the particular part of the system usually neglected-automatic pose initialization. Finally, we show how a number of existing approaches can successfully be integrated in a system that is able to recognize and grasp fairly textured, everyday objects. One of the examples presented in the experimental section shows a mobile robot performing tasks in a real-word environment-a living room.

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Automatic dismantling integrating optical flow into a machine vision-controlled robot system

Cited by 15 publications

References 10 publications

Object recognition and full pose registration from a single image for robotic manipulation

Object recognition and full pose registration from a single image for robotic manipulation

Object recognition and pose estimation using color cooccurrence histograms and geometric modeling

Robust Visual Servoing

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