Rethinking Robot Vision – Combining Shape and Appearance

Schlemmer, Matthias J.; Biegelbauer, G.; Vincze, Markus

doi:10.5772/5691

Cited by 10 publications

(6 citation statements)

References 25 publications

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“…[7], [8], [9]). In particular, Ye et al [10] have considered modeling the variation in viewpoint when observing a specific object and learning this model from training data.…”

Section: Related Workmentioning

confidence: 99%

Viewpoint detection models for sequential embodied object category recognition

Meger

Gupta

Little

2010

2010 IEEE International Conference on Robotics and Automation

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Abstract-This paper proposes a method for learning viewpoint detection models for object categories that facilitate sequential object category recognition and viewpoint planning. We have examined such models for several state-of-the-art object detection methods. Our learning procedure has been evaluated using an exhaustive multiview category database recently collected for multiview category recognition research. Our approach has been evaluated on a simulator that is based on real images that have previously been collected. Simulation results verify that our viewpoint planning approach requires fewer viewpoints for confident recognition. Finally, we illustrate the applicability of our method as a component of a completely autonomous visual recognition platform that has previously been demonstrated in an object category recognition competition.

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“…[7], [8], [9]). In particular, Ye et al [10] have considered modeling the variation in viewpoint when observing a specific object and learning this model from training data.…”

Section: Related Workmentioning

confidence: 99%

Viewpoint detection models for sequential embodied object category recognition

Meger

Gupta

Little

2010

2010 IEEE International Conference on Robotics and Automation

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“…Regarding the second issue, pattern recognition and image processing have inspired different methods for clustering 3D points. Thus, simple methods have been broadly used to support mobile robot operation extracting planar structures, or more compact models [7] [8] [9]. Another possibility is to formulate the 3D clustering problem using a probabilistic approach, e.g., using Mixture Models [10].…”

Section: Introductionmentioning

confidence: 99%

A Novel Robust Scene Change Detection Algorithm for Autonomous Robots Using Mixtures of Gaussians

Manso

Núñez

Silva

et al. 2014

International Journal of Advanced Robotic Systems

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Interest in change detection techniques has considerably increased during recent years in the field of autonomous robotics. This is partly because changes in a robot's working environment are useful for several robotic skills (e.g., spatial cognition, modelling or navigation) and applications (e.g., surveillance or guidance robots). Changes are usually detected by comparing current data provided by the robot's sensors with a previously known map or model of the environment. When the data consists of a large point cloud, dealing with it is a computationally expensive task, mainly due to the amount of points and the redundancy. Using Gaussian Mixture Models (GMM) instead of raw point clouds leads to a more compact feature space that can be used to efficiently process the input data. This allows us to successfully segment the set of 3D points acquired by the sensor and reduce the computational load of the change detection algorithm. However, the segmentation of the environment as a Mixture of Gaussians has some problems that need to be properly addressed. In this paper, a novel change detection algorithm is described in order to improve the robustness and computational cost of previous approaches. The proposal is based on the classic Expectation Maximization (EM) algorithm, for which different selection criteria are evaluated. As demonstrated in the experimental results section, the proposed change detection algorithm achieves the detection of changes in the robot's working environment faster and more accurately than similar approaches.

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“…shapes of objects etc. can be obtained from the vision cameras [2] and a further detail can be obtained by moving them around the object. But, moving the robot around the object is not always possible, as can happen with humans as well.…”

Section: Introductionmentioning

confidence: 99%

System approach: A paradigm for robotic tactile sensing

Dahiya

Valle

Metta

2008

2008 10th IEEE International Workshop on Advanced Motion Control

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In the pursuit of developing touch sensors or tactile sensing arrays, the emphasis has only been on the sensors. This led to a large number of 'bench top' sensors, very few of which have actually been used in robotic systems. And those that have seen the actual use have almost invariably been used in static contact point imaging rather than the active manipulation or exploration. Perhaps the lack of the system approach rendered many of them unusable. In this work, we present the design of a tactile sensing system taking into account not only the parameters to be sensed but also the physical and operational constraints of robotic system.

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Rethinking Robot Vision – Combining Shape and Appearance

Cited by 10 publications

References 25 publications

Viewpoint detection models for sequential embodied object category recognition

Viewpoint detection models for sequential embodied object category recognition

A Novel Robust Scene Change Detection Algorithm for Autonomous Robots Using Mixtures of Gaussians

System approach: A paradigm for robotic tactile sensing

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