Kinect range sensing: Structured-light versus Time-of-Flight Kinect

Sarbolandi, Hamed; Lefloch, Damien; Kolb, Andreas

doi:10.1016/j.cviu.2015.05.006

Cited by 340 publications

(266 citation statements)

References 45 publications

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“…These figures, also show the error at increasing target distances (0.5m, 1m, and 2m). As it is known [37], the Kinect accuracy is not very good and degrades with distance. However, our calibration method yields better accuracy than the Kinect's built-in mapping.…”

Section: D Reconstructionmentioning

confidence: 99%

Multiple Kinect V2 Calibration

Córdova-Esparza

Terven²,

Hernandez

et al. 2016

Automatika

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In this paper, we propose a method to easily calibrate multiple Kinect V2 sensors. It requires the cameras to simultaneously observe a 1D object shown at different orientations (three at least) or a 2D object for at least one acquisition. This is possible due to the built-in coordinate mapping capabilities of the Kinect. Our method follows five steps: image acquisition, pre-calibration, point cloud matching, intrinsic parameters initialization, and final calibration. We modeled radial and distortion parameters of all the cameras, obtaining a root mean square reprojection error of 0.2 pixels on the depth cameras and 0.4 pixels on the color cameras. To validate the calibration results we performed point cloud fusion with color and 3D reconstruction using the depth and color information from four Kinect sensors.Key words: Camera calibration, Kinect V2 system, Multiple cameras Umjeravanje Kinect V2 sustava s više kamera. U ovom je radu predložena metoda za jednostavno umjeravanje proizvoljnog broja senzora Kinect V2. Izvodi se istovremenim snimanjem objekta s više kamera. Jednodimenzionalan objekt potrebno je snimiti s najmanje 3 različite orijentacije, a dvodimenzionalan s najmanje jedne orijentacije. Istovremeno snimanje s više kamera moguće je zahvaljujući integriranom mapiranju koordinata u Kinect sustavu. Predložena metoda izvodi se u pet koraka: akvizicija slike, predumjeravanje, uskladivanje oblaka točaka, inicijalizacija intrinzičnih parametara i konačno umjeravanje. U radu su modelirani radijalni i distorzijski parametri svih kamera, pričemu se ostvaruje korijen srednje kvadratične pogreške ponovne projekcije iznosa 0:2 piksela na kamerama dubine i 0:4 piksela na kamerama u boji. Za validaciju rezultata umjeravanja provedena je fuzija oblaka točaka s rekonstrukcijom trodimenzionalnog objekta i boje korištenjem informacije o dubini i boji š cetiri Kinect senzora.

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Section: D Reconstructionmentioning

confidence: 99%

Multiple Kinect V2 Calibration

Córdova-Esparza

Terven²,

Hernandez

et al. 2016

Automatika

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“…When compared to the Kinect v1, the Kinect v2 is better suited for applications that require high precision and accuracy at longer ranges (e.g. face gesture recognition of a static person at a depth of 1.2 meters, as suggested by Sarbolandi et al [92]). Nonetheless, ToF cameras suffer from various systematic and stochastic error sources, including ambient background light, temperature drift, and internal scattering.…”

Section: Depth Sensor Classesmentioning

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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“…Compared to RGB-D cameras based on time-of-flight technology (e.g., Kinect for Xbox One), the structured light sensors (e.g., Kinect for Xbox 360) are sensitive to illumination. This limits their applicability in direct sunlight [17]. …”

Section: Use Of Rgb-d Camerasmentioning

confidence: 99%

Extracting Semantic Information from Visual Data: A Survey

Liu

et al. 2016

Robotics

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Abstract:The traditional environment maps built by mobile robots include both metric ones and topological ones. These maps are navigation-oriented and not adequate for service robots to interact with or serve human users who normally rely on the conceptual knowledge or semantic contents of the environment. Therefore, the construction of semantic maps becomes necessary for building an effective human-robot interface for service robots. This paper reviews recent research and development in the field of visual-based semantic mapping. The main focus is placed on how to extract semantic information from visual data in terms of feature extraction, object/place recognition and semantic representation methods.

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Kinect range sensing: Structured-light versus Time-of-Flight Kinect

Cited by 340 publications

References 45 publications

Multiple Kinect V2 Calibration

Multiple Kinect V2 Calibration

Optimal 6D Object Pose Estimation with Commodity Depth Sensors

Extracting Semantic Information from Visual Data: A Survey

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