Indoor Scene Point Cloud Registration Algorithm Based on RGB-D Camera Calibration

Tsai, Chi-Yi

doi:10.3390/s17081874

Cited by 16 publications

(6 citation statements)

References 36 publications

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“…RGB and IR images will not be spatially registered since the optical characteristics of the sensors and their relative position and orientation differ from one another. This will be another barrier to overcome since the lack of information between RGB and thermal cameras makes it difficult to perform an extrinsic calibration using typical techniques [20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Assessment of Registration Methods for Thermal Infrared and Visible Images for Diabetic Foot Monitoring

González‐Pérez

Strom

Arteaga-Marrero

et al. 2021

Sensors

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This work presents a revision of four different registration methods for thermal infrared and visible images captured by a camera-based prototype for the remote monitoring of diabetic foot. This prototype uses low cost and off-the-shelf available sensors in thermal infrared and visible spectra. Four different methods (Geometric Optical Translation, Homography, Iterative Closest Point, and Affine transform with Gradient Descent) have been implemented and analyzed for the registration of images obtained from both sensors. All four algorithms’ performances were evaluated using the Simultaneous Truth and Performance Level Estimation (STAPLE) together with several overlap benchmarks as the Dice coefficient and the Jaccard index. The performance of the four methods has been analyzed with the subject at a fixed focal plane and also in the vicinity of this plane. The four registration algorithms provide suitable results both at the focal plane as well as outside of it within 50 mm margin. The obtained Dice coefficients are greater than 0.950 in all scenarios, well within the margins required for the application at hand. A discussion of the obtained results under different distances is presented along with an evaluation of its robustness under changing conditions.

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Section: Introductionmentioning

confidence: 99%

Assessment of Registration Methods for Thermal Infrared and Visible Images for Diabetic Foot Monitoring

González‐Pérez

Strom

Arteaga-Marrero

et al. 2021

Sensors

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show abstract

“…The purpose of camera calibration is to acquire the parameters of the camera imaging model, including the intrinsic parameters that characterize the intrinsic structure of the camera and the optical characteristics of the lens and the external parameters that describe the spatial pose of the camera [18]. Moreover, camera calibration methods can be roughly divided into four categories: laboratory calibration [19], optical calibration [20], on-the-job calibration [21], and self-calibration [22]. The calibration field in laboratory calibration is generally composed of some control points with known spatial coordinates, and it can also be subdivided into a 2D calibration field and a 3D calibration field.…”

Section: Introductionmentioning

confidence: 99%

A 3D Compensation Method for the Systematic Errors of Kinect V2

Zhao

2021

Remote Sensing

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To reduce the 3D systematic error of the RGB-D camera and improve the measurement accuracy, this paper is the first to propose a new 3D compensation method for the systematic error of a Kinect V2 in a 3D calibration field. The processing of the method is as follows. First, the coordinate system between the RGB-D camera and 3D calibration field is transformed using 3D corresponding points. Second, the inliers are obtained using the Bayes SAmple Consensus (BaySAC) algorithm to eliminate gross errors (i.e., outliers). Third, the parameters of the 3D registration model are calculated by the iteration method with variable weights that can further control the error. Fourth, three systematic error compensation models are established and solved by the stepwise regression method. Finally, the optimal model is selected to calibrate the RGB-D camera. The experimental results show the following: (1) the BaySAC algorithm can effectively eliminate gross errors; (2) the iteration method with variable weights could better control slightly larger accidental errors; and (3) the 3D compensation method can compensate 91.19% and 61.58% of the systematic error of the RGB-D camera in the depth and 3D directions, respectively, in the 3D control field, which is superior to the 2D compensation method. The proposed method can control three types of errors (i.e., gross errors, accidental errors and systematic errors) and model errors and can effectively improve the accuracy of depth data.

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“…The introduction of low-cost RGB-D cameras created an opportunity for 3D reconstruction of scenes to be performed at consumer-level. The increasing popularity of these sensors promoted the research of their use to reconstruct 3D indoor scenes [14][15][16]. Their manoeuvrability, permitting hand-held operation and allowing the user to get closer to parts of the scene and capturing them from different angles, proves to be an advantage in indoor environments with high probability of object occlusion, when compared with fixed high resolution scanners, which usually require more space and fixed poses to operate.…”

Section: Introductionmentioning

confidence: 99%

Enhancement of RGB-D Image Alignment Using Fiducial Markers

Madeira

Oliveira

Dias

2020

Sensors

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Three-dimensional (3D) reconstruction methods generate a 3D textured model from the combination of data from several captures. As such, the geometrical transformations between these captures are required. The process of computing or refining these transformations is referred to as alignment. It is often a difficult problem to handle, in particular due to a lack of accuracy in the matching of features. We propose an optimization framework that takes advantage of fiducial markers placed in the scene. Since these markers are robustly detected, the problem of incorrect matching of features is overcome. The proposed procedure is capable of enhancing the 3D models created using consumer level RGB-D hand-held cameras, reducing visual artefacts caused by misalignments. One problem inherent to this solution is that the scene is polluted by the markers. Therefore, a tool was developed to allow their removal from the texture of the scene. Results show that our optimization framework is able to significantly reduce alignment errors between captures, which results in visually appealing reconstructions. Furthermore, the markers used to enhance the alignment are seamlessly removed from the final model texture.

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Indoor Scene Point Cloud Registration Algorithm Based on RGB-D Camera Calibration

Cited by 16 publications

References 36 publications

Assessment of Registration Methods for Thermal Infrared and Visible Images for Diabetic Foot Monitoring

Assessment of Registration Methods for Thermal Infrared and Visible Images for Diabetic Foot Monitoring

A 3D Compensation Method for the Systematic Errors of Kinect V2

Enhancement of RGB-D Image Alignment Using Fiducial Markers

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