Depth Errors Analysis and Correction for Time-of-Flight (ToF) Cameras

He, Ying; Liang, Bin; Zou, Yu; He, Jin; Yang, Jun

doi:10.3390/s17010092

Cited by 72 publications

(35 citation statements)

References 36 publications

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“…the Kinect 2.0), which we confirmed to be several mm at a distance of about 2 m (inset to Fig.4a; Supplementary Fig. 16) 51 . By converting this depth variation into an equivalent delay time (details in Supplementary Note 2 and Supplementary Fig.…”

Section: Such Behavior Of the Ste Emission In Terms Of Pl Lifetime Asupporting

confidence: 53%

High-resolution remote thermometry and thermography using luminescent low-dimensional tin-halide perovskites

Yakunin¹,

Benin²,

Shynkarenko³

et al. 2019

Nat. Mater.

279

258

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bolometric detectors, owing to advancements in MEMS-technologies (Micro-Electro-Mechanical Systems) 6,7 , have already entered the consumer electronics market, and are able to record thermal images with both high speed and high resolution. However, their thermographic performance, based on measurements of IR radiation intensity, is inherently limited by the transparency and emissivity/reflectivity of an observed object and, more importantly, by any material and medium (window, coating, matrix, solvent etc.) situated within the path between the detector and an object ( Fig. 1). As one of the major consequences, IR thermography cannot be easily combined with conventional optical microscopy or other enclosed optical systems such as cryostats or microfluidic cells.An alternative method for remote thermography, which is unhindered by enclosures or IRabsorptive media, utilizes temperature sensitive luminophores (i.e. fluorophores or phosphors) with PL in the visible spectral range (Fig. 1c) that are deposited onto, or incorporated into, the object of interest as temperature probes [8][9][10][11][12][13][14][15][16] . To probe an object's temperature, the luminophore is then excited by an ultraviolet or visible (UV-Vis) pulsed source (e.g. laser or light-emitting diode) and the temperature-dependent PL lifetime decay is then analyzed by time-resolving detectors.This PL-lifetime approach exhibits several benefits: the excitation power and, consequently the PL intensity, can be adjusted to a value appropriate for the dynamic range of the detector.Additionally, the use of UV-Vis light, rather than mid-to long-wavelength IR radiation, allows for the direct integration of this method with conventional optical spectroscopy and microscopy applied in biological studies and materials research. Furthermore, higher spatial resolutions can be obtained with visible light (400-700 nm) as the diffraction-limit is ca. 20-times sharper than for LWIR (7-14 µm); this potentially extends the utility of remote thermography to intracellular, in vitro, and in vivo studies 17 .

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Section: Such Behavior Of the Ste Emission In Terms Of Pl Lifetime Asupporting

confidence: 53%

High-resolution remote thermometry and thermography using luminescent low-dimensional tin-halide perovskites

Yakunin¹,

Benin²,

Shynkarenko³

et al. 2019

Nat. Mater.

279

258

View full text Add to dashboard Cite

show abstract

“…As reported in the well-known survey study about ToF cameras [40], systems based on ToF camera are always subjected to encounter certain types of errors, such as systematic and non-systematic errors. Therefore, such systems always need complex error correction or calibration algorithms to fix these errors [41].…”

Section: Related Workmentioning

confidence: 99%

Indoor Positioning System: A New Approach Based on LSTM and Two Stage Activity Classification

et al. 2019

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The number of studies on the development of indoor positioning systems has increased recently due to the growing demands of the various location-based services. Inertial sensors available in commercial smartphones play an important role in indoor localization and navigation owing to their highly accurate localization performance. In this study, the inertial sensors of a smartphone, which generate distinct patterns for physical activities and action units (AUs), are employed to localize a target in an indoor environment. These AUs, (such as a left turn, right turn, normal step, short step, or long step), help to accurately estimate the indoor location of a target. By taking advantage of sophisticated deep learning algorithms, we propose a novel approach for indoor navigation based on long short-term memory (LSTM). The LSTM accurately recognizes physical activities and related AUs by automatically extracting the efficient features from the distinct patterns of the input data. Experiment results show that LSTM provides a significant improvement in the indoor positioning performance through the recognition task. The proposed system achieves a better localization performance than the trivial fingerprinting method, with an average error of 0.782 m in an indoor area of 128.6 m2. Additionally, the proposed system exhibited robust performance by excluding the abnormal activity from the pedestrian activities.

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“…Although such method is simple and requires little computation, the correction result is limited. Ying He et al [5] .attempted to introduce the data learning process into the error analysis of RGB-D cameras. Freedman D. et al proposed to improve the accuracy of depth data by using multiple acquisition frequencies.…”

Section: Related Workmentioning

confidence: 99%

A General Means for Depth Data Error Estimation of Depth Sensors

She¹,

Yu²

2019

Proceedings of the 2019 International Conference on Computer, Network, Communication and Information Systems (CNCI 2019)

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At present the depth error estimation of the RGB-D sensor is aimed at a specific depth camera, the disadvantages of these methods is that it is put forword for a specific sensor and cannot be applied to the other sensors. In order to solve this problem, this paper proposes a general method to estimate the root mean square (RMS) error of depth data provided by general three-dimensional sensors. The method is applicable to three-dimensional sensors based on structured light, time-of-flight, stereo vision and other technologies. Use a common checkerboard to detect corner points and get two point clouds, one is the real point cloud of the image corner, and the other is the estimated point cloud of the corner point given by the device. After registrating of the two point clouds, the RMS error is calculated. The RMS error is generalized as a function of the distance between the RGB-D sensor and the checkerboard .The accuracy and practicability of the proposed method are verified by comparing it with the existing advanced depth error estimation methods.

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Depth Errors Analysis and Correction for Time-of-Flight (ToF) Cameras

Cited by 72 publications

References 36 publications

High-resolution remote thermometry and thermography using luminescent low-dimensional tin-halide perovskites

High-resolution remote thermometry and thermography using luminescent low-dimensional tin-halide perovskites

Indoor Positioning System: A New Approach Based on LSTM and Two Stage Activity Classification

A General Means for Depth Data Error Estimation of Depth Sensors

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