Multi-target super-resolution using compressive sensing arguments for multipath interference recovery

Xuan, Vinh Nguyen; Hartmann, Klaus; Weihs, W.; Loffeld, Otmar

doi:10.1109/cosera.2016.7745718

Cited by 6 publications

(9 citation statements)

References 5 publications

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“…One of the most common problems the ToF technology faces is multipath interference (MPI) [10], [11], [13], [17], [18], [19], [20], [21], [22], [23], [24], [27]. Light is reflected multiple times in the target scene, and an object reflects not only the modulated light emitted by the camera, but also light from other indirect paths.…”

Section: A Motivationmentioning

confidence: 99%

“…The results provided in [23] showed that the calculation efficiency of matrix pencil method is five times that of OMP. In [24], multifrequency measurements are combined with a compressive sensing (CS) algorithm to reconstruct the sparse signal and thus retrieve the true depth. Instead of [24], which is a rather unfeasible approach, Heredia Conde et al tuned and simplified the recovery process leveraging the knowledge of the structured sparsity of the recovered signal in [25] and [26], obtaining better accuracy and reducing the runtime compared to pure OMP recovery.…”

Section: B Related Workmentioning

confidence: 99%

“…In [24], multifrequency measurements are combined with a compressive sensing (CS) algorithm to reconstruct the sparse signal and thus retrieve the true depth. Instead of [24], which is a rather unfeasible approach, Heredia Conde et al tuned and simplified the recovery process leveraging the knowledge of the structured sparsity of the recovered signal in [25] and [26], obtaining better accuracy and reducing the runtime compared to pure OMP recovery. However, this CS framework is considered not feasible because it involves not only large memory requirements but also heavy computational load [27].…”

Section: B Related Workmentioning

confidence: 99%

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Evaluation and Compensation of the Effect of Dirt on Time-of-Flight 3D Imaging Systems

Liu

Conde

2022

IEEE Sensors J.

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In the past few years, 3D imaging technology has received a lot of attention. Time-of-Flight (ToF) cameras based on the indirect ToF principle calculate depth information by measuring the phase shift between the emitted periodic signal and the reflected signal. When the illumination from the modulated light source reaches the same pixel through multiple paths, multipath interference will occur. Acquisitions at multiple modulation frequencies are then required to separate the interfering paths. Parametric spectral estimation methods, such as Prony's method and its robust variants, have shown success in resolving multiple paths from multi-frequency measurements. With the widespread usage of ToF cameras in various fields, the operating environment of the camera should be taken into account in order to obtain accurate 3D images.In this work, we aim to study the effect of dirt on 3D imaging systems quantitatively. To this end, ten sheets with different transmittances, between 50% and 100% are used. To the best of our knowledge, for the first time we provide standardized methods for evaluating the effect of dirt on protective covers in the depth estimation of "flash lidar". The accuracy of depth measurements obtained by multi-frequency multipath recovery in the absence of dust is up to 99%. However, when the light transmittance of the protective cover is less than 90%, the measurement result is already unreliable. The experiments have shown that the depth measurements obtained using the multi-frequency estimation method are more accurate in the presence of dust than the single-frequency estimation method.

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Section: A Motivationmentioning

confidence: 99%

Section: B Related Workmentioning

confidence: 99%

Section: B Related Workmentioning

confidence: 99%

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Evaluation and Compensation of the Effect of Dirt on Time-of-Flight 3D Imaging Systems

Liu

Conde

2022

IEEE Sensors J.

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“…8). Several methods have been proposed to identify and correct the jump edge errors [92]- [95]. Pathak et al [96] used Gaussian analysis in correction of multi-modal measurements.…”

Section: B Nonsystematic Error Reduction 1) Depth Data Denoisingmentioning

confidence: 99%

Recent Advances in 3D Data Acquisition and Processing by Time-of-Flight Camera

Chen

2019

IEEE Access

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Three-dimensional (3D) data acquisition and real-time processing is a critical issue in an artificial vision system. The developing time-of-flight (TOF) camera as a real-time vision sensor for obtaining depth images has now received wide attention, due to its great potential in many areas, such as 3D perception, computer vision, robot navigation, human-machine interaction, augmented reality, and so on. This paper survey advances in TOF imaging technology mainly from the last decade. We focus only on recent progress of overcoming limitations such as systematic errors, object boundary ambiguity, multipath error, phase wrapping, and motion blur, and address the theoretical principles and future research trends as well. INDEX TERMS Vision sensor, time of flight, depth image, sensing device, computer vision, 3D vision.

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“…One special case of LiDAR interference that has been studied extensively is the multi-path interference (MPI) [18][19][20][21], which is a LiDAR system interfering with itself through a plurality of optical paths, either as a consequence of the complex geometry of the imaged scene or the presence of semi-transparent or specular objects. Published approaches to MPI suppression include the use of multiple modulation frequencies in indirect time-of-flight (i-ToF) [22], sometimes in conjunction with compressive sensing to reconstruct multi-path reflections [23].…”

Section: Related Workmentioning

confidence: 99%

An Optical Interference Suppression Scheme for TCSPC Flash LiDAR Imagers

Carrara¹,

Fiergolski²

2019

Applied Sciences

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This paper describes an optical interference suppression scheme that allows flash light detection and ranging (LiDAR) imagers to run safely and reliably in uncontrolled environments where multiple LiDARs are expected to operate concurrently. The issue of optical interference is a potential show-stopper for the adoption of flash LiDAR as a technology of choice in multi-user application fields such as automotive sensing and autonomous vehicle navigation. The relatively large emission angle and field of view of flash LiDAR imagers make them especially vulnerable to optical interference. This work illustrates how a time-correlated single-photon counting LiDAR can control the timing of its laser emission to reduce its statistical correlation to other modulated or pulsed light sources. This method is based on a variable random delay applied to the laser pulse generated by LiDAR and to the internal circuitry measuring the time-of-flight. The statistical properties of the pseudorandom sequence of delays determines the effectiveness of LiDAR resilience against unintentional and intentional optical interference. For basic multi-camera operation, a linear feedback shift register (LFSR) was used as a random delay generator, and the performance of the interference suppression was evaluated as a function of sequence length and integration time. Direct interference from an identical LiDAR emitter pointed at the same object was reduced up to 50 dB. Changing integration time between 10 ms and 100 ms showed a marginal impact on the performance of the suppression (less than 3 dB deviation). LiDAR signal integrity was characterized during suppression, obtaining a maximum relative deviation of the measured time-of-flight of 0.1%, and a maximum deviation of measurements spread (full-width half-maximum) of 3%. The LiDAR signal presented an expected worst-case reduction in intensity of 25%.

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Multi-target super-resolution using compressive sensing arguments for multipath interference recovery

Cited by 6 publications

References 5 publications

Evaluation and Compensation of the Effect of Dirt on Time-of-Flight 3D Imaging Systems

Evaluation and Compensation of the Effect of Dirt on Time-of-Flight 3D Imaging Systems

Recent Advances in 3D Data Acquisition and Processing by Time-of-Flight Camera

An Optical Interference Suppression Scheme for TCSPC Flash LiDAR Imagers

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