A New Calibration Method for Commercial RGB-D Sensors

Darwish, Walid; Tang, Shengjun; Li, Wenbin; Chen, Wu

doi:10.3390/s17061204

Cited by 38 publications

(58 citation statements)

References 24 publications

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“…To date, there are numerous RGB-D sensors on the market such as the Xtion Pro from Asus (2012), the second-generation Kinect from Microsoft (2013), Structure Sensor (2014), and the RealSense Series from Intel (2015). Although they were first introduced for the game industry where the accuracy of the measurements is not crucial, RGB-D sensors have been applied for many high accuracy applications recently, for example, indoor 3D modeling [15], simultaneous localization and mapping (SLAM) [16], and augmented reality applications [17], etc., in which the rigorous calibration and error modeling of RGB-D sensor data become increasingly essential [18].For the 3D modeling of point cloud data, the extraction of regular shapes (i.e., plane, sphere, and cylinder) from point coordinates is an important step for the usage of the point cloud. For example, many studies have tried to develop plane fitting methods for depth calibration [19,20], object segmentation [9,10], and 3D environment reconstruction [21][22][23].…”

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confidence: 99%

Improving Plane Fitting Accuracy with Rigorous Error Models of Structured Light-Based RGB-D Sensors

Darwish

et al. 2020

Remote Sensing

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Plane fitting is a fundamental operation for point cloud data processing. Most existing methods for point cloud plane fitting have been developed based on high-quality Lidar data giving equal weight to the point cloud data. In recent years, using low-quality RGB-Depth (RGB-D) sensors to generate 3D models has attracted much attention. However, with low-quality point cloud data, equal weight plane fitting methods are not optimal as the range errors of RGB-D sensors are distance-related. In this paper, we developed an accurate plane fitting method for a structured light (SL)-based RGB-D sensor. First, we derived an error model of a point cloud dataset from the SL-based RGB-D sensor through error propagation from the raw measurement to the point coordinates. A new cost function based on minimizing the radial distances with the derived rigorous error model was then proposed for the random sample consensus (RANSAC)-based plane fitting method. The experimental results demonstrated that our method is robust and practical for different operating ranges and different working conditions. In the experiments, for the operating ranges from 1.23 meters to 4.31 meters, the mean plane angle errors were about one degree, and the mean plane distance errors were less than six centimeters. When the dataset is of a large-depth-measurement scale, the proposed method can significantly improve the plane fitting accuracy, with a plane angle error of 0.5 degrees and a mean distance error of 4.7 cm, compared to 3.8 degrees and 16.8 cm, respectively, from the conventional un-weighted RANSAC method. The experimental results also demonstrate that the proposed method is applicable for different types of SL-based RGB-D sensor. The rigorous error model of the SL-based RGB-D sensor is essential for many applications such as in outlier detection and data authorization. Meanwhile, the precise plane fitting method developed in our research will benefit algorithms based on high-accuracy plane features such as depth calibration, 3D feature-based simultaneous localization and mapping (SLAM), and the generation of indoor building information models (BIMs).

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confidence: 99%

Improving Plane Fitting Accuracy with Rigorous Error Models of Structured Light-Based RGB-D Sensors

Darwish

et al. 2020

Remote Sensing

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“…Landsat satellites have maintained high geometric and radiometric accuracy and have provided science with quality data to support various research and operational applications and are used as the "gold standard" for the calibration of other commercial sensors [62,63]. Looking beyond Landsat 9, the United States Geological Survey (USGS) has collected user needs that emphasize the improvement in spatial, temporal, and spectral resolution in future Landsat data products.…”

Section: Implications For Future Landsat Missionsmentioning

confidence: 99%

Assessment of the Impacts of Image Signal-to-Noise Ratios in Impervious Surface Mapping

Xian

Shi²,

Anderson

et al. 2019

Remote Sensing

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Medium spatial resolution satellite images are frequently used to characterize thematic land cover and a continuous field at both regional and global scales. However, high spatial resolution remote sensing data can provide details in landscape structures, especially in the urban environment. With upgrades to spatial resolution and spectral coverage for many satellite sensors, the impact of the signal-to-noise ratio (SNR) in characterizing a landscape with highly heterogeneous features at the sub-pixel level is still uncertain. This study used WorldView-3 (WV3) images as a basis to evaluate the impacts of SNR on mapping a fractional developed impervious surface area (ISA). The point spread function (PSF) from the Landsat 8 Operational Land Imager (OLI) was used to resample the WV3 images to three different resolutions: 10 m, 20 m, and 30 m. Noise was then added to the resampled WV3 images to simulate different fractional levels of OLI SNRs. Furthermore, regression tree algorithms were incorporated into these images to estimate the ISA at different spatial scales. The study results showed that the total areal estimate could be improved by about 1% and 0.4% at 10-m spatial resolutions in our two study areas when the SNR changes from half to twice that of the Landsat OLI SNR level. Such improvement is more obvious in the high imperviousness ranges. The root-mean-square-error of ISA estimates using images that have twice and two-thirds the SNRs of OLI varied consistently from high to low when spatial resolutions changed from 10 m to 20 m. The increase of SNR, however, did not improve the overall performance of ISA estimates at 30 m.

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“…Various calibration methods, particularly for the depth sensor, have been studied by different research groups [14].…”

Section: Camera Calibrationmentioning

confidence: 99%

Dynamic Collision Avoidance System for a Manipulator Based on RGB-D Data

Brito

Lima

Costa

et al. 2017

ROBOT 2017: Third Iberian Robotics Conference

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Abstract. The new paradigms of Industry 4.0 demand the collaboration between robot and humans. They could help and collaborate each other without any additional safety unlike other manipulators. The robot should have the ability of acquire the environment and plan (or re-plan) on-the-fly the movement avoiding the obstacles and people. This paper proposes a system that acquires the environment space, based on a kinect sensor, performs the path planning of a UR5 manipulator for pick and place tasks while avoiding the objects, based on the point cloud from kinect. Results allow to validate the proposed system.

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A New Calibration Method for Commercial RGB-D Sensors

Cited by 38 publications

References 24 publications

Improving Plane Fitting Accuracy with Rigorous Error Models of Structured Light-Based RGB-D Sensors

Improving Plane Fitting Accuracy with Rigorous Error Models of Structured Light-Based RGB-D Sensors

Assessment of the Impacts of Image Signal-to-Noise Ratios in Impervious Surface Mapping

Dynamic Collision Avoidance System for a Manipulator Based on RGB-D Data

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