An EM/E-MRF algorithm for adaptive model based tracking in extremely poor visibility

Stolkin, Rustam; Greig, Alistair; Hodgetts, Mark; Gilby, J.

doi:10.1016/j.imavis.2007.06.008

Cited by 35 publications

(21 citation statements)

References 43 publications

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“…This motion is either assumed to occur [6], [7], [8] or can be induced by the robot [9], [10]. Although relative motion is a strong cue for segmentation, generating this motion in an unknown pile is oftentimes dangerous and undesirable.…”

Section: A Object Segmentationmentioning

confidence: 99%

Perceiving, learning, and exploiting object affordances for autonomous pile manipulation

et al. 2014

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Abstract-Autonomous manipulation in unstructured environments presents roboticists with three fundamental challenges: object segmentation, action selection, and motion generation. These challenges become more pronounced when unknown manmade or natural objects are cluttered together in a pile. We present an end-to-end approach to the problem of manipulating unknown objects in a pile, with the objective of removing all objects from the pile and placing them into a bin. Our robot perceives the environment with an RGB-D sensor, segments the pile into objects using non-parametric surface models, computes the affordances of each object, and selects the best affordance and its associated action to execute. Then, our robot instantiates the proper compliant motion primitive to safely execute the desired action. For efficient and reliable action selection, we developed a framework for supervised learning of manipulation expertise. We conducted dozens of trials and report on several hours of experiments involving more than 1500 interactions. The results show that our learning-based approach for pile manipulation outperforms a common sense heuristic as well as a random strategy, and is on par with human action selection.

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Section: A Object Segmentationmentioning

confidence: 99%

Perceiving, learning, and exploiting object affordances for autonomous pile manipulation

et al. 2014

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“…In reference [3], a moving target detecting method based on regional maximum posterior probability is proposed and reference [4] gives out a moving target detecting method which combined newly established MRF model of double-scale neighborhood with the dividing algorithm improved from non-linear transformation. In reference [5], color cluster of original image is used as a priori knowledge to redefine energy function to achieve moving target detection and in reference [6], EM/E-MRF adaptive model is applied in target detection and good result is achieved.…”

Section: Applications Of Markov Random Field Theory In Target Detectionmentioning

confidence: 99%

Study on the Application of Markov Random Field in Target Tracking

Sun¹,

Zhang²

2015

Advances in Intelligent Systems Research

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Abstract. Being a key link in modern imaging tracking system, the quality of moving target detection in dynamic scene will influence the performance of the whole tracking system immediately. Markov Random Field theory has been successfully used in image target tracking and identification leaning against its complete mathematical theory. In this paper, characteristics of Markov Random Field theory have been discussed and the application in target detection has been analyzed, which supplies some reference to fundamental research and implementation.

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“…Segmentation from motion algorithms analyze sequences of images in which objects are in motion. This motion is either assumed to occur [8], [21], [25] or can be induced by the robot [16]. Relative motion is a conclusive clue for object segmentation.…”

Section: A Scene Segmentationmentioning

confidence: 99%

Clearing a Pile of Unknown Objects using Interactive Perception

Katz¹,

Kazemi²,

Bagnell³

et al. 2012

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Abstract-We address the problem of clearing a pile of unknown objects using an autonomous interactive perception approach. Our robot hypothesizes the boundaries of objects in a pile of unknown objects (object segmentation) and verifies its hypotheses (object detection) using deliberate interactions. To guarantee the safety of the robot and the environment, we use compliant motion primitives for poking and grasping. Every verified segmentation hypothesis can be used to parameterize a compliant controller for manipulation or grasping. The robot alternates between poking actions to verify its segmentation and grasping actions to remove objects from the pile. We demonstrate our method with a robotic manipulator. We evaluate our approach with real-world experiments of clearing cluttered scenes composed of unknown objects.

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An EM/E-MRF algorithm for adaptive model based tracking in extremely poor visibility

Cited by 35 publications

References 43 publications

Perceiving, learning, and exploiting object affordances for autonomous pile manipulation

Perceiving, learning, and exploiting object affordances for autonomous pile manipulation

Study on the Application of Markov Random Field in Target Tracking

Clearing a Pile of Unknown Objects using Interactive Perception

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