ToF-sensors: New dimensions for realism and interactivity

Kolb, Andreas; Barth, Erhardt; Koch, Reinhard

doi:10.1109/cvprw.2008.4563159

Cited by 49 publications

(25 citation statements)

References 27 publications

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“…ToF cameras emit modulated light, and depth computation is based on timed measures of the reflected light signal [14]. The modulation frequency of the emitted light determines the ambiguity-free range of the sensor, usually between 5 and 7 meters.…”

Section: Tof Camerasmentioning

confidence: 99%

Object modeling using a ToF camera under an uncertainty reduction approach

Foix

Alenyà

Andrade‐Cetto

et al. 2010

2010 IEEE International Conference on Robotics and Automation

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Abstract-Time-of-Flight (ToF) cameras deliver 3D images at 25 fps, offering great potential for developing fast object modeling algorithms. Surprisingly, this potential has not been extensively exploited up to now. A reason for this is that, since the acquired depth images are noisy, most of the available registration algorithms are hardly applicable. A further difficulty is that the transformations between views are in general not accurately known, a circumstance that multi-view object modeling algorithms do not handle properly under noisy conditions. In this work, we take into account both uncertainty sources (in images and camera poses) to generate spatially consistent 3D object models fusing multiple views with a probabilistic approach. We propose a method to compute the covariance of the registration process, and apply an iterative state estimation method to build object models under noisy conditions.

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Section: Tof Camerasmentioning

confidence: 99%

Object modeling using a ToF camera under an uncertainty reduction approach

Foix

Alenyà

Andrade‐Cetto

et al. 2010

2010 IEEE International Conference on Robotics and Automation

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“…160×120 pixels with depth information for each pixel at 25fps using ToF (time-of-flight) principle. ToF cameras emit modulated light, and depth computation is based on measures on the reflected light using the well known time-of-flight principle (Kolb et al, 2008). Fig.…”

Section: The Observation Modelmentioning

confidence: 99%

Planning Stacking Operations With an Unknown Number of Objects

Trilla¹,

Alenyà

2010

Proceedings of the 7th International Conference on Informatics in Control, Automation and Robotics

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Abstract:A planning framework is proposed for the task of cleaning a table and stack an unknown number of objects of different size on a tray. We propose to divide this problem in two, and combine two different planning algorithms. One, plan hand motions in the euclidean space to be able to move the hand in a noisy scenario using a novel Time-of-Flight camera (ToF) to perform the perception of the environment. The other one, chooses the strategy to effectively clean the table, considering the symbolic position of the objects, and also its size for stacking considerations. Our formulation does not use information about the number of objects available, and thus is general in this sense. Also, it can deal with different object sizes, planning adequately to stack them. The special definition of the possible actions allows a simple and elegant way of characterizing the problem, and is one of the key ingredients of the proposed solution. Some experiments are provided in simulated and real scenarios that validate our approach.

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“…3D cameras have several advantages, including their usability and their ability to produce depth images or point clouds in real time. They are used for many applications, such as user interaction (gesture recognition) as mentioned in (Kolb et al, 2008), pattern recognition (Kolb et al, 2009) or scene analysis in robotics (May et al, 2006). The marketing of the Microsoft Kinect in 2010 and of the Asus Xtion Pro in 2011, both based on the technology developed by PrimeSense (Arieli et al, 2010), led to the wide dissemination of this kind of sensors.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Localization using RGB-D cameras orthoimages

Mittet

Landes

Grussenmeyer

2014

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Commission 5WG V, ICWG I/Va KEY WORDS: mobile mapping, system, orthoimage, urban environment, range imaging camera ABSTRACT:3D cameras are a new generation of sensors more and more used in geomatics. The main advantages of 3D cameras are their handiness, their price, and the ability to produce range images or point clouds in real-time. They are used in many areas and the use of this kind of sensors has grown especially as the Kinect (Microsoft) arrived on the market. This paper presents a new localization system based exclusively on the combination of several 3D cameras on a mobile platform. It is planed that the platform moves on sidewalks, acquires the environment and enables the determination of most appropriate routes for disabled persons. The paper will present the key features of our approach as well as promising solutions for the challenging task of localization based on 3D-cameras. We give examples of mobile trajectory estimated exclusively from 3D cameras acquisitions. We evaluate the accuracy of the calculated trajectory, thanks to a reference trajectory obtained by a total station.

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ToF-sensors: New dimensions for realism and interactivity

Cited by 49 publications

References 27 publications

Object modeling using a ToF camera under an uncertainty reduction approach

Object modeling using a ToF camera under an uncertainty reduction approach

Planning Stacking Operations With an Unknown Number of Objects

Localization using RGB-D cameras orthoimages

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