Active 3D Object Localization Using a Humanoid Robot

Andreopoulos, Alexander; Hasler, Stephan; Wersing, Heiko; Janßen, H.; Tsotsos, John K.; Körner, Edgar

doi:10.1109/tro.2010.2090058

Cited by 48 publications

(30 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Their performance must be robust, reliable and safe. At this point the humanoid robots focus on the control of their movements and their perceptual abilities are very limited (but see Andreopoulos et al 2011). Robot use in hotels and hospitals is also very limited.…”

Section: Why Has the Robotics Community Failed To Produce A Flexible mentioning

confidence: 99%

Revisiting active perception

2017

Self Cite

View full text Add to dashboard Cite

Despite the recent successes in robotics, artificial intelligence and computer vision, a complete artificial agent necessarily must include active perception. A multitude of ideas and methods for how to accomplish this have already appeared in the past, their broader utility perhaps impeded by insufficient computational power or costly hardware. The history of these ideas, perhaps selective due to our perspectives, is presented with the goal of organizing the past literature and highlighting the seminal contributions. We argue that those contributions are as relevant today as they were decades ago and, with the state of modern computational tools, are poised to find new life in the robotic perception systems of the next decade.

show abstract

Section: Why Has the Robotics Community Failed To Produce A Flexible mentioning

confidence: 99%

Revisiting active perception

2017

Self Cite

View full text Add to dashboard Cite

show abstract

“…A particular example of a practical robotic vision system that employs certain attentive processes is presented in [46]. In addition, the problem of actively searching for an object in a 3D environment is studied under the constraint of a maximum search time using a visually guided humanoid robot with 26 degrees of freedom [47]. Another study follows the standard pattern recognition approach based on four main steps [48]: (i) preprocessing to achieve colour constancy and stereo pair calibration; (ii) segmentation using depth-continuity information; (iii) feature extraction based on visual saliency; and (iv) classification using a neural network.…”

Section: Active Visionmentioning

confidence: 99%

A Taxonomy of Vision Systems for Ground Mobile Robots

Gómez

Fernández-Caballero

García-Varea

et al. 2014

International Journal of Advanced Robotic Systems

View full text Add to dashboard Cite

This paper introduces a taxonomy of vision systems for ground mobile robots. In the last five years, a significant number of relevant papers have contributed to this subject. Firstly, a thorough review of the papers is proposed to discuss and classify both past and the most current approaches in the field. As a result, a global picture of the state of the art of the last five years is obtained. Moreover, the study of the articles is used to put forward a comprehensive taxonomy based on the most up-to-date research in ground mobile robotics. In this sense, the paper aims at being especially helpful to both budding and experienced researchers in the areas of vision systems and mobile ground robots. The taxonomy described is devised from a novel perspective, namely in order to respond to the main questions posed when designing robotic vision systems: why?, what for?, what with?, how?, and where? The answers are derived from the most relevant techniques described in the recent literature, leading in a natural way to a series of classifications that are discussed and contextualized. The article offers a global picture of the state of the art in the area and discovers some promising research lines.

show abstract

“…Some of these models have also been applied to enhance common techniques like face recognition by using biologically-inspired features [6]. Some research draw more attention to active-vision systems, which have been used to solve different vision problems like: object recognition [7], [8], [9], [10], [11]; visual search [12], [13]; visual attention [14]; or visual tracking [15]. It has also been investigated how to integrate object recognition [16], [17] and visual attention also with a focus on the aspect of computational complexity [18].…”

Section: Related Workmentioning

confidence: 99%

A concurrent real-time biologically-inspired visual object recognition system

Holzbach

Cheng

2014

2014 IEEE International Conference on Robotics and Automation (ICRA)

View full text Add to dashboard Cite

In this paper, we present an biologically-motivated object recognition system for robots and vision tasks in general. Our approach is based on a hierarchical model of the visual cortex for feature extraction and rapid scene categorization. We modify this static model to be usable in time-crucial realworld scenarios by applying methods for optimization from signal detection theory, information theory, signal processing and linear algebra. Our system is more robust to clutter and supports object localization by approaching the binding problem in contrast to previous models. We show that our model outperforms the preceding model and that by our modifications we created a robust and fast system which integrates the capabilities of biological-inspired object recognition in a technical application.

show abstract

Active 3D Object Localization Using a Humanoid Robot

Cited by 48 publications

References 35 publications

Revisiting active perception

Revisiting active perception

A Taxonomy of Vision Systems for Ground Mobile Robots

A concurrent real-time biologically-inspired visual object recognition system

Contact Info

Product

Resources

About