Calibration of Kinect for Xbox One and Comparison between the Two Generations of Microsoft Sensors

Abstract

In recent years, the videogame industry has been characterized by a great boost in gesture recognition and motion tracking, following the increasing request of creating immersive game experiences. The Microsoft Kinect sensor allows acquiring RGB, IR and depth images with a high frame rate. Because of the complementary nature of the information provided, it has proved an attractive resource for researchers with very different backgrounds. In summer 2014, Microsoft launched a new generation of Kinect on the market, based on time-of-flight technology. This paper proposes a calibration of Kinect for Xbox One imaging sensors, focusing on the depth camera. The mathematical model that describes the error committed by the sensor as a function of the distance between the sensor itself and the object has been estimated. All the analyses presented here have been conducted for both generations of Kinect, in order to quantify the improvements that characterize every single imaging sensor. Experimental results show that the quality of the delivered model improved applying the proposed calibration procedure, which is applicable to both point clouds and the mesh model created with the Microsoft Fusion Libraries.

On the calibration of active binocular and RGBD vision systems for dual-arm robots

“…It is evident that the distance measurements delivered by this sensor are much more precise than the ASUS Xtion Pro. However, the Kinect V2 reconstruction error is approximately 20mm at a distance of 3 m [15].…”

3D Analysis of Upper Limbs Motion During Rehabilitation Exercises Using the Kinect^TM Sensor: Development, Laboratory Validation and Clinical Application

“…Subjects were in underwear to allow reliable placement of the markers for the MBS analysis taking place simultaneously. The Kinect v2 could not be used due to the interference between the projected speckle pattern and the infrared signals from the MBS [14].…”

“…In total 19 local coordinate systems (LCS), following ISB recommendations [27] for axis orientation, were located in the origins indicated by numbers (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19) in Figure 1. Then 33 LCSs origin motions relative to parent LCSs (Table 1) Each of 33 trajectory plots was processed to assess different properties of the shape created by hodograph.…”

“…However research has been done to compare the two generations of Kinect for object detection the results were better for the second generation of Kinect especially when the distance between the object and the camera increases [14]. The use of Kinect V2 to assess upper limb mobility or function has also been extensively studied.…”

3d Models for All: Low-Cost Acquisition Through Mobile Devices In Comparison With Image Based Techniques. Potentialities And Weaknesses in Cultural Heritage Domain

“…The second approach (2) requires additional sensors (such as Occipital Structure Sensor, Intel Real Sense, EORA 3D) that need to be connected with handheld devices. The technology that gives the ability to understand the distance to objects in the real world is defined as "Depth Perception": the use of an RGB camera, an infrared (IR) projector and IR sensor (Pagliari et al, 2015) are required. Manufacturers' devices can choose from common depth technologies, including Structured Light, Time of Flight, and Stereo (Smisek et al, 2011), in order to implement Depth Perception.…”