Metrological evaluation of Microsoft Kinect and Asus Xtion sensors

González-Jorge, H.; Riveiro, Belén; Vázquez-Fernández, Esteban; Martínez-Sánchez, J.; Arias, Pedro

doi:10.1016/j.measurement.2013.01.011

Cited by 136 publications

(73 citation statements)

References 12 publications

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“…43, No 1, 2015 FME Transactions involved identification of parameters of the sensor camera. Gonzales-Jorge et al [8] investigated accuracy of uncalibrated Kinect XBOX 360 and Asus Xtion using a specially designed measurement fixture. Their measurements confirm that both accuracy and precision deteriorate with the distance.…”

Section: Related Workmentioning

confidence: 99%

Calibration of Kinect-type RGB-D sensors for robotic applications

Karan

2015

FME Transaction

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Section: Related Workmentioning

confidence: 99%

Calibration of Kinect-type RGB-D sensors for robotic applications

Karan

2015

FME Transaction

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“…It contains two CMOS sensors, one for the RGB image (640 x 480 pixels at 30 fps) and another for the infrared image from which the depth map is calculated, based on the deformation of an infrared projected pattern (λ = 830nm). The depth sensor has an optimal utilisation in a range of 1.2 meter (precision better than 10 mm) to 3.5 m (precision better than 30 mm) [13] and can be perturbed by other sources of infrared light.…”

Section: Ms Kinect Solution (Kinectsdk)mentioning

confidence: 99%

Head pose estimation & TV Context: current technology

Rocca¹,

Mancaş²,

Grisard³

et al. 2015

EAI Endorsed Transactions on Creative Technologies

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With the arrival of low-cost high quality cameras, implicit user behaviour tracking is easier and it becomes very interesting for viewer modelling and content personalization in a TV context. In this paper, we present a comparison between three common algorithms of automatic head direction extraction for a person watching TV in a realistic context. Those algorithms compute the different rotation angles of the head (pitch, roll, yaw) in a non-invasive and continuous way based on 2D and/or 3D features acquired with low cost cameras. These results are compared with a reference based on the Qualisys motion capture commercial system which is a robust marker-based tracking system. The performances of the different algorithms are compared function of different configurations. While our results show that full implicit behaviour tracking in real-life TV setups is still a challenge, with the arrival of next generation sensors (as the new Kinect one sensor), accurate TV personalization based on implicit behaviour is close to become a very interesting option.

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“…However, due to their noise sensitivity and resolution limitations the scientific community is still cautious regarding the usage of RGB-D cameras on projects that require outcomes of high accuracy, e.g. monitoring, geometric recording and modeling of cultural heritage assets and sites (Gonzalez-Jorge et al, 2013;Lachat et al, 2015). Indeed, depth cameras have been mostly used for indoor scene projects due to their sensitivity in noise, minimum and maximum distance to object constraints, restricted field of view and connectivity limitations.…”

Section: Introductionmentioning

confidence: 99%

DEPTH CAMERAS ON UAVs: A FIRST APPROACH

Deris¹,

Trigonis²,

Aravanis³

et al. 2017

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

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ABSTRACT:Accurate depth information retrieval of a scene is a field under investigation in the research areas of photogrammetry, computer vision and robotics. Various technologies, active, as well as passive, are used to serve this purpose such as laser scanning, photogrammetry and depth sensors, with the latter being a promising innovative approach for fast and accurate 3D object reconstruction using a broad variety of measuring principles including stereo vision, infrared light or laser beams. In this study we investigate the use of the newly designed Stereolab's ZED depth camera based on passive stereo depth calculation, mounted on an Unmanned Aerial Vehicle with an ad-hoc setup, specially designed for outdoor scene applications. Towards this direction, the results of its depth calculations and scene reconstruction generated by Simultaneous Localization and Mapping (SLAM) algorithms are compared and evaluated based on qualitative and quantitative criteria with respect to the ones derived by a typical Structure from Motion (SfM) and Multiple View Stereo (MVS) pipeline for a challenging cultural heritage application.

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Metrological evaluation of Microsoft Kinect and Asus Xtion sensors

Cited by 136 publications

References 12 publications

Calibration of Kinect-type RGB-D sensors for robotic applications

Calibration of Kinect-type RGB-D sensors for robotic applications

Head pose estimation & TV Context: current technology

DEPTH CAMERAS ON UAVs: A FIRST APPROACH

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