Photogrammetric Bundle Adjustment With Self-Calibration of the PrimeSense 3D Camera Technology: Microsoft Kinect

Chow, Jacky C. K.; Lichti, Derek D.

doi:10.1109/access.2013.2271860

Cited by 35 publications

(41 citation statements)

References 26 publications

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“…The object boundaries are not well defined and some vertical bands are also present within the map as already reported in previous works (Menna et al, 2011;Chow and Lichti, 2013). …”

Section: Image Sensors Data Analysismentioning

confidence: 73%

“…The Kinect sensor has always attracted research from different fields -from robotics (El-Iaithy et al, 2012, Oliver et al, 2012 to Computer Vision (Han et al, 2013), from biomedical engineering (Alnowami et al, 2012, Guevara et al, 2013 to archaeology (Richards-Rissetto et al, 2012) -due to its 3D capabilities and low-cost. In a short while Software Development Kit (SDK) realized by third-party communities have been released enabling to use the device not only as a game device, but also as a measurement device (Khoshelham, 2011;Menna et al, 2011;Mankhoff et al, 2012;Chow and Lichti, 2013). In June 2011, Microsoft released its official control libraries and SDK for full body motion capture, facial and vocal recognition.…”

Section: Introductionmentioning

confidence: 99%

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Kinect Fusion improvement using depth camera calibration

Pagliari

Menna

Roncella

et al. 2014

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

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ABSTRACT:Scene's 3D modelling, gesture recognition and motion tracking are fields in rapid and continuous development which have caused growing demand on interactivity in video-game and e-entertainment market. Starting from the idea of creating a sensor that allows users to play without having to hold any remote controller, the Microsoft Kinect device was created. The Kinect has always attract researchers in different fields, from robotics to Computer Vision (CV) and biomedical engineering as well as third-party communities that have released several Software Development Kit (SDK) versions for Kinect in order to use it not only as a game device but as measurement system. Microsoft Kinect Fusion control libraries (firstly released in March 2013) allow using the device as a 3D scanning and produce meshed polygonal of a static scene just moving the Kinect around. A drawback of this sensor is the geometric quality of the delivered data and the low repeatability. For this reason the authors carried out some investigation in order to evaluate the accuracy and repeatability of the depth measured delivered by the Kinect. The paper will present a throughout calibration analysis of the Kinect imaging sensor, with the aim of establishing the accuracy and precision of the delivered information: a straightforward calibration of the depth sensor in presented and then the 3D data are correct accordingly. Integrating the depth correction algorithm and correcting the IR camera interior and exterior orientation parameters, the Fusion Libraries are corrected and a new reconstruction software is created to produce more accurate models.

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“…The object boundaries are not well defined and some vertical bands are also present within the map as already reported in previous works (Menna et al, 2011;Chow and Lichti, 2013). …”

Section: Image Sensors Data Analysismentioning

confidence: 73%

Section: Introductionmentioning

confidence: 99%

Kinect Fusion improvement using depth camera calibration

Pagliari

Menna

Roncella

et al. 2014

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

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“…Flat surfaces do not exhibit dot occlusion nor dot spreading, however, so depth errors arise mainly when noisy IR intensities lead to imperfect matches from the SAD algorithm in the simulation. And since Chow et al [25] demonstrate that error due to disparity quantization is minimal, the experimental standard errors near the center of the focal plane are decidedly attributed to the sub-pixel refinement step.…”

Section: Model Validationmentioning

confidence: 99%

“…For instance, since the Kinect diffuser unit contains two DOEs arranged in series to generate and propagate the distribution of bright and dark spots into a 3 × 3 grid of sub-patterns, significant pincushion and moderate barrel distortion occurs. Lens distortion can potentially be accounted for by using Brown's model [20] to estimate radial parameters, which has been previously adopted in [55] and [25]. Additionally, a treatment of the bright center dots as anchor points could be incorporated to determine the 3D rotation required to align the 3 × 3 grids.…”

Section: Ir Pattern Dependent Calibrationmentioning

confidence: 99%

Optimal 6D Object Pose Estimation with Commodity Depth Sensors

Landau

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Accurate 6D object pose estimation, as well as other model-based shape matching objectives such as object detection, classification, and shape inspection, is prominent and necessary in a large number of domains and applications. This includes household and robotics applications to automate various tasks, where input depth measurements are aligned to a corresponding model of the object, such as a CAD that is composed of several interconnected parts, depending on the model's level of detail and complexity. Industrial metrology systems also compare the shapes of engineered components and equipment to their corresponding 3D models for quality assessment and anomaly detection; this often requires very costly and high resolution projection and detection mechanisms. Structuredlight laser scanners have, however, emerged as a viable option to provide cheap and reasonably accurate depth estimates, which have recently become commodity-priced within this decade due to the maturity of the technology. These sensors are also robust for semi-transparent and reflective surfaces, dynamic scenery, ambient background light, and temperature drift. Certain structured-light light coding methods unfortunately need to implement a complicated, non-invertible transformation of a distorted light pattern to generate the pixel-by-pixel depth estimates, thereby leading to a loss of information. This can result in poorly estimated or even missing depth data, especially when the object has small 3D features or non-ideal surface properties. Moreover, the informative rigid body constraint is blurred when the nonlinear 2D depth image to 3D pseudo-measurement point cloud transformation is applied, which is the domain where most model-based shape matching methods operate.Alternatively, estimating pose from information early in the structured-light sensing/processing chain has the potential to alleviate errors induced in subsequent nonlinear processing steps that are in fact locally scene dependent. This motivates one of the main contributions from this dissertation, which presents an asymptotically optimal maximum likelihood estimation method that operates directly on the raw output IR images. This is made possible by an extensive study on a commonly utilized commodity-priced structured-light sensor, which contributed to the proposed high-fidelity IR and depth image predictor and simulator that models the physics of the transmit and receive optics,

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“…Considering especially the ToF based KinectV2 used in this paper, the analysis of related error sources is reported in existing surveys [35,36]. The IR and color optical sensors inside the Kinect V2 could be modeled using the pinhole camera model and solved by well-established RGB camera calibration methods [9,34,37]. The calibration of the relative pose parameters between the IR and RGB cameras is similar to stereo-camera calibration that could be solved by measuring geometrical targets in the shared FOV (e.g., 2D planar pattern [38,39], circle grid [40], 1D target [41,42]), appearance-based methods [43], or self-calibration used in robot navigation [44][45][46].…”

Section: Introductionmentioning

confidence: 99%

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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Photogrammetric Bundle Adjustment With Self-Calibration of the PrimeSense 3D Camera Technology: Microsoft Kinect

Cited by 35 publications

References 26 publications

Kinect Fusion improvement using depth camera calibration

Kinect Fusion improvement using depth camera calibration

Optimal 6D Object Pose Estimation with Commodity Depth Sensors

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

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