First Experiences With Kinect V2 Sensor for Close Range 3d Modelling

Lachat, Elise; Macher, H.; Mittet, Marie-Anne; Landes, Tania; Grussenmeyer, Pierre

doi:10.5194/isprsarchives-xl-5-w4-93-2015

Cited by 168 publications

(120 citation statements)

References 13 publications

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“…It is learned from the statistics that increasing the frame amount makes the range errors uniform rather than enhancing the precision. Similar conclusions can be found in existing studies [73,74]. Considering both the data quality and data gathering efficiency, the amount of averaging successive depth map frames was empirically set to 100 frames (3.3 s observation) in the calibration process of this study.…”

Section: Intrinsic Calibration Results Of Single Rgb-d Sensormentioning

confidence: 52%

Calibrate Multiple Consumer RGB-D Cameras for Low-Cost and Efficient 3D Indoor Mapping

et al. 2018

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Abstract:Traditional indoor laser scanning trolley/backpacks with multi-laser scanner, panorama cameras, and an inertial measurement unit (IMU) installed are a popular solution to the 3D indoor mapping problem. However, the cost of those mapping suits is quite expensive, and can hardly be replicated by consumer electronic components. The consumer RGB-Depth (RGB-D) camera (e.g., Kinect V2) is a low-cost option for gathering 3D point clouds. However, because of the narrow field of view (FOV), its collection efficiency and data coverages are lower than that of laser scanners. Additionally, the limited FOV leads to an increase of the scanning workload, data processing burden, and risk of visual odometry (VO)/simultaneous localization and mapping (SLAM) failure. To find an efficient and low-cost way to collect 3D point clouds data with auxiliary information (i.e., color) for indoor mapping, in this paper we present a prototype indoor mapping solution that is built upon the calibration of multiple RGB-D sensors to construct an array with large FOV. Three time-of-flight (ToF)-based Kinect V2 RGB-D cameras are mounted on a rig with different view directions in order to form a large field of view. The three RGB-D data streams are synchronized and gathered by the OpenKinect driver. The intrinsic calibration that involves the geometry and depth calibration of single RGB-D cameras are solved by homography-based method and ray correction followed by range biases correction based on pixel-wise spline line functions, respectively. The extrinsic calibration is achieved through a coarse-to-fine scheme that solves the initial exterior orientation parameters (EoPs) from sparse control markers and further refines the initial value by an iterative closest point (ICP) variant minimizing the distance between the RGB-D point clouds and the referenced laser point clouds. The effectiveness and accuracy of the proposed prototype and calibration method are evaluated by comparing the point clouds derived from the prototype with ground truth data collected by a terrestrial laser scanner (TLS). The overall analysis of the results shows that the proposed method achieves the seamless integration of multiple point clouds from three Kinect V2 cameras collected at 30 frames per second, resulting in low-cost, efficient, and high-coverage 3D color point cloud collection for indoor mapping applications.

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Section: Intrinsic Calibration Results Of Single Rgb-d Sensormentioning

confidence: 52%

Calibrate Multiple Consumer RGB-D Cameras for Low-Cost and Efficient 3D Indoor Mapping

et al. 2018

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“…The RGB and depth cameras have a higher resolution and the depth measurements are more accurate. Also, the Kinect V2 uses the time-of-flight principle, whereas the Kinect V1 is based on structured light to provide the depth data [21]. Fig.…”

Section: Proposed Methodologymentioning

confidence: 99%

Recognition and Position Estimation for Multiple Labware Transportation Using Kinect V2 and Mobile Robots

Ali¹,

Liu²,

Stoll³

et al. 2017

Adv. sci. technol. eng. syst. j.

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“…We realized an experimental setup similar to the ones presented in Lachat et al (2015), but we also extended our analysis outdoor. In order to evaluate the effect of different materials and of the surface roughness, a panel with a number of square samples with a side of 0.2 m has been realized (see Figure 4).…”

Section: The Experimental Set-upmentioning

confidence: 99%

“…The reference system has been considered with its origin corresponding to the IR camera projection centre, with the x-axis direct along the width of the IR imaging sensor, the y-axis direct along the height of the IR imaging sensor and the z-axis to complete the right handed Cartesian system. The estimated parameters with the corresponding precision are reported in Table 3 Kinect v2 is characterized by higher quality imaging sensors and the new device is a huge improvement over the first generation for indoor applications, see Pagliari and Pinto (2015), Lachat et al (2015). Furthermore, the new generation of Kinect is capable of acquiring data even outdoor, so it is important to study the accuracy and the precision of the depth data acquired under the influence of sunlight radiation.…”

Section: Geometrical Calibration Of the Optic Sensors And Depth Accuracymentioning

confidence: 99%

Integration of Kinect and Low-Cost GNSS for Outdoor Navigation

Pagliaria¹,

Pinto²,

Reguzzoni³

et al. 2016

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

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ABSTRACT:Since its launch on the market, Microsoft Kinect sensor has represented a great revolution in the field of low cost navigation, especially for indoor robotic applications. In fact, this system is endowed with a depth camera, as well as a visual RGB camera, at a cost of about 200$. The characteristics and the potentiality of the Kinect sensor have been widely studied for indoor applications. The second generation of this sensor has been announced to be capable of acquiring data even outdoors, under direct sunlight. The task of navigating passing from an indoor to an outdoor environment (and vice versa) is very demanding because the sensors that work properly in one environment are typically unsuitable in the other one. In this sense the Kinect could represent an interesting device allowing bridging the navigation solution between outdoor and indoor. In this work the accuracy and the field of application of the new generation of Kinect sensor have been tested outdoor, considering different lighting conditions and the reflective properties of the emitted ray on different materials. Moreover, an integrated system with a low cost GNSS receiver has been studied, with the aim of taking advantage of the GNSS positioning when the satellite visibility conditions are good enough. A kinematic test has been performed outdoor by using a Kinect sensor and a GNSS receiver and it is here presented.

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First Experiences With Kinect V2 Sensor for Close Range 3d Modelling

Cited by 168 publications

References 13 publications

Calibrate Multiple Consumer RGB-D Cameras for Low-Cost and Efficient 3D Indoor Mapping

Calibrate Multiple Consumer RGB-D Cameras for Low-Cost and Efficient 3D Indoor Mapping

Recognition and Position Estimation for Multiple Labware Transportation Using Kinect V2 and Mobile Robots

Integration of Kinect and Low-Cost GNSS for Outdoor Navigation

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