Harmonic distortion free distance estimation in ToF camera

Kang, Byongmin; Kim, Seong‐Jin; Lee, Seungkyu; Lee, Keechang; Kim, James D. K.; Kim, Chang Yeong

doi:10.1117/12.872313

Cited by 15 publications

(12 citation statements)

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“…Distance estimation. To estimate distance from vehicle to the scene's objects, many sensors are available, e.g., laser-ultrasonic [12] and time of flight sensors [13]. These kinds of sensors are widely used in such contexts, yet the use of multiple types of sensors, distance sensors with cameras makes the system more complex and expensive.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning for Real-Time 3D Multi-Object Detection, Localisation, and Tracking: Application to Smart Mobility

Mauri

Khemmar

Decoux

et al. 2020

Sensors

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In core computer vision tasks, we have witnessed significant advances in object detection, localisation and tracking. However, there are currently no methods to detect, localize and track objects in road environments, and taking into account real-time constraints. In this paper, our objective is to develop a deep learning multi object detection and tracking technique applied to road smart mobility. Firstly, we propose an effective detector-based on YOLOv3 which we adapt to our context. Subsequently, to localize successfully the detected objects, we put forward an adaptive method aiming to extract 3D information, i.e., depth maps. To do so, a comparative study is carried out taking into account two approaches: Monodepth2 for monocular vision and MADNEt for stereoscopic vision. These approaches are then evaluated over datasets containing depth information in order to discern the best solution that performs better in real-time conditions. Object tracking is necessary in order to mitigate the risks of collisions. Unlike traditional tracking approaches which require target initialization beforehand, our approach consists of using information from object detection and distance estimation to initialize targets and to track them later. Expressly, we propose here to improve SORT approach for 3D object tracking. We introduce an extended Kalman filter to better estimate the position of objects. Extensive experiments carried out on KITTI dataset prove that our proposal outperforms state-of-the-art approches.

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Section: Related Workmentioning

confidence: 99%

Deep Learning for Real-Time 3D Multi-Object Detection, Localisation, and Tracking: Application to Smart Mobility

Mauri

Khemmar

Decoux

et al. 2020

Sensors

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“…(2). 10 As suggested by Kang et al, 9 the harmonic distortion can be alleviated by an elaborated modeling of the actual form of the waves. Finally, the measured distance is also altered by an integration time and reflectivity related offset, for which the exact reasons are unknown yet.…”

Section: Introductionmentioning

confidence: 97%

“…One of the drawbacks that hinder the use of ToF cameras in more various applications is that the measured distance d suffers from error originating from various sources such as noise, 8 harmonic distortion, 9 and reflectivity/integration time related deviations. 10 The photon shot noise causes time-varying error in measured distances, and the amount is closely related with f , B, A, and T : 8…”

Section: Introductionmentioning

confidence: 99%

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Wide range time-of-flight camera: design, analysis, and simulation

Choi

2013

Three-Dimensional Image Processing (3DIP) and Applications 2013

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Time-of-flight cameras measure the distances to scene points by emitting and detecting a modulated infrared light signal. The modulation frequency of the signal determines a certain maximum range within which the measured distance is unambiguous. If the actual distance to a scene point is longer than the maximum range, the measured distance suffers from phase wrapping, which makes the measured distance to be shorter than its actual distance by an unknown multiple of the maximum range. This paper proposes a time-of-flight camera, which is capable of restoring the actual distance by simultaneously emitting light signals of different modulation frequencies and detecting them separately in different regions of the sensor. We analyze the noise characteristic of the camera, and acquire simulated depth maps using a commercially available time-of-flight camera, reflecting the increased amount of noise due to the use of dual-frequency signals. We finally propose a phase unwrapping method for restoring the actual distances from such a dual-frequency depth map. Through experiments, we demonstrate that the proposed method is capable of extending the maximum range to at least twice, with high success rates.

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“…Other approaches to range and amplitude determination have included shape fitting methods [6], deconvolution based approaches [7] and piecewise equations which assume that the correlation waveform is a perfect triangle waveform [8,9]. However, none of these approaches have gained general acceptance.…”

Section: Modelling Samplingmentioning

confidence: 99%

Understanding and Ameliorating Non-Linear Phase and Amplitude Responses in AMCW Lidar

2011

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Amplitude modulated continuous wave (AMCW) lidar systems commonly suffer from non-linear phase and amplitude responses due to a number of known factors such as aliasing and multipath inteference. In order to produce useful range and intensity information it is necessary to remove these perturbations from the measurements. We review the known causes of non-linearity, namely aliasing, temporal variation in correlation waveform shape and mixed pixels/multipath inteference. We also introduce other sources of non-linearity, including crosstalk, modulation waveform envelope decay and non-circularly symmetric noise statistics, that have been ignored in the literature. An experimental study is conducted to evaluate techniques for mitigation of non-linearity, and it is found that harmonic cancellation provides a significant improvement in phase and amplitude linearity.

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Harmonic distortion free distance estimation in ToF camera

Cited by 15 publications

References 0 publications

Deep Learning for Real-Time 3D Multi-Object Detection, Localisation, and Tracking: Application to Smart Mobility

Deep Learning for Real-Time 3D Multi-Object Detection, Localisation, and Tracking: Application to Smart Mobility

Wide range time-of-flight camera: design, analysis, and simulation

Understanding and Ameliorating Non-Linear Phase and Amplitude Responses in AMCW Lidar

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