Wavefront sensing based depth sensor for macroscopic objects

2020 ◽  
Author(s):  
Yicheng Wu ◽  
Fengqiang Li ◽  
Florian Willomitzer ◽  
Ashok Veeraraghavan ◽  
Oliver Cossairt
Keyword(s):  
Wavefront Sensing ◽  
Depth Sensor ◽  
Macroscopic Objects

Quantum fluctuations have been shown to affect macroscopic objects

Nature ◽  
2020 ◽  
Vol 583 (7814) ◽  
pp. 31-32
Author(s):  
Valeria Sequino ◽  
Mateusz Bawaj
Keyword(s):  
Quantum Fluctuations ◽  
Macroscopic Objects

scholarly journals A Tip–Tilt and Piston Detection Approach for Segmented Telescopes

Photonics ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. 3
Author(s):  
Shun Qin ◽  
Wai Kin Chan
Keyword(s):  
Error Detection ◽  
Phase Retrieval ◽  
Dynamic Range ◽  
Detection Accuracy ◽  
Wavefront Sensing ◽  
Next Generation ◽  
Large Aperture ◽  
Detection Approach ◽  
Segmented Mirror ◽  
And Control

Accurate segmented mirror wavefront sensing and control is essential for next-generation large aperture telescope system design. In this paper, a direct tip–tilt and piston error detection technique based on model-based phase retrieval with multiple defocused images is proposed for segmented mirror wavefront sensing. In our technique, the tip–tilt and piston error are represented by a basis consisting of three basic plane functions with respect to the x, y, and z axis so that they can be parameterized by the coefficients of these bases; the coefficients then are solved by a non-linear optimization method with the defocus multi-images. Simulation results show that the proposed technique is capable of measuring high dynamic range wavefront error reaching 7λ, while resulting in high detection accuracy. The algorithm is demonstrated as robust to noise by introducing phase parameterization. In comparison, the proposed tip–tilt and piston error detection approach is much easier to implement than many existing methods, which usually introduce extra sensors and devices, as it is a technique based on multiple images. These characteristics make it promising for the application of wavefront sensing and control in next-generation large aperture telescopes.

scholarly journals Computational Method for Wavefront Sensing Based on Transport-Of-Intensity Equation

Photonics ◽  
2021 ◽  
Vol 8 (6) ◽  
pp. 177
Author(s):  
Iliya Gritsenko ◽  
Michael Kovalev ◽  
George Krasin ◽  
Matvey Konoplyov ◽  
Nikita Stsepuro
Keyword(s):  
A Priori ◽  
Spherical Wave ◽  
Computational Method ◽  
Optical Systems ◽  
Radius Of Curvature ◽  
Imaging Method ◽  
Wavefront Sensing ◽  
Phase Imaging ◽  
Transport Of Intensity Equation ◽  
Priori Information

Recently the transport-of-intensity equation as a phase imaging method turned out as an effective microscopy method that does not require the use of high-resolution optical systems and a priori information about the object. In this paper we propose a mathematical model that adapts the transport-of-intensity equation for the purpose of wavefront sensing of the given light wave. The analysis of the influence of the longitudinal displacement z and the step between intensity distributions measurements on the error in determining the wavefront radius of curvature of a spherical wave is carried out. The proposed method is compared with the traditional Shack–Hartmann method and the method based on computer-generated Fourier holograms. Numerical simulation showed that the proposed method allows measurement of the wavefront radius of curvature with radius of 40 mm and with accuracy of ~200 μm.

Hartmann wavefront sensing of the corrective optics for the Hubble Space Telescope

1994 ◽  
Author(s):  
Pamela S. Davila ◽  
William L. Eichhorn ◽  
Mark E. Wilson
Keyword(s):  
Hubble Space Telescope ◽  
Wavefront Sensing ◽  
Space Telescope

scholarly journals Robust autonomous flight in cluttered environment using a depth sensor

2020 ◽  
Vol 12 ◽  
pp. 175682932092452
Author(s):  
Liang Lu ◽  
Alexander Yunda ◽  
Adrian Carrio ◽  
Pascual Campoy
Keyword(s):  
High Speed ◽  
Point Clouds ◽  
Depth Sensor ◽  
Cluttered Environments ◽  
Industrial Plants ◽  
Cluttered Environment ◽  
Navigation Strategy ◽  
Physical Sensors ◽  
Aerial Vehicle ◽  
Planning Objective

This paper presents a novel collision-free navigation system for the unmanned aerial vehicle based on point clouds that outperform compared to baseline methods, enabling high-speed flights in cluttered environments, such as forests or many indoor industrial plants. The algorithm takes the point cloud information from physical sensors (e.g. lidar, depth camera) and then converts it to an occupied map using Voxblox, which is then used by a rapid-exploring random tree to generate finite path candidates. A modified Covariant Hamiltonian Optimization for Motion Planning objective function is used to select the best candidate and update it. Finally, the best candidate trajectory is generated and sent to a Model Predictive Control controller. The proposed navigation strategy is evaluated in four different simulation environments; the results show that the proposed method has a better success rate and a shorter goal-reaching distance than the baseline method.

scholarly journals BLAINDER—A Blender AI Add-On for Generation of Semantically Labeled Depth-Sensing Data

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2144
Author(s):  
Stefan Reitmann ◽  
Lorenzo Neumann ◽  
Bernhard Jung
Keyword(s):  
Point Clouds ◽  
Training Data ◽  
Sensor Data ◽  
Generation Process ◽  
Data Generation ◽  
Depth Sensor ◽  
Depth Sensing ◽  
Depth Sensors ◽  
3D Point Clouds ◽  
Wide Range

Common Machine-Learning (ML) approaches for scene classification require a large amount of training data. However, for classification of depth sensor data, in contrast to image data, relatively few databases are publicly available and manual generation of semantically labeled 3D point clouds is an even more time-consuming task. To simplify the training data generation process for a wide range of domains, we have developed the BLAINDER add-on package for the open-source 3D modeling software Blender, which enables a largely automated generation of semantically annotated point-cloud data in virtual 3D environments. In this paper, we focus on classical depth-sensing techniques Light Detection and Ranging (LiDAR) and Sound Navigation and Ranging (Sonar). Within the BLAINDER add-on, different depth sensors can be loaded from presets, customized sensors can be implemented and different environmental conditions (e.g., influence of rain, dust) can be simulated. The semantically labeled data can be exported to various 2D and 3D formats and are thus optimized for different ML applications and visualizations. In addition, semantically labeled images can be exported using the rendering functionalities of Blender.

scholarly journals Classical sub-subleading soft photon and soft graviton theorems in four spacetime dimensions

2020 ◽  
Vol 2020 (12) ◽  
Author(s):  
Biswajit Sahoo
Keyword(s):  
Fourier Transformation ◽  
Soft Photon ◽  
Scattering Process ◽  
Retarded Time ◽  
Long Wavelength ◽  
Macroscopic Objects ◽  
Loop Amplitudes

Abstract Classical soft photon and soft graviton theorems determine long wavelength electromagnetic and gravitational waveforms for a general classical scattering process in terms of the electric charges and asymptotic momenta of the ingoing and outgoing macroscopic objects. Performing Fourier transformation of the electromagnetic and gravitational waveforms in the frequency variable one finds electromagnetic and gravitational waveforms at late and early retarded time. Here extending the formalism developed in [1], we derive sub-subleading electromagnetic and gravitational waveforms which behave like u−2(ln u) at early and late retarded time u in four spacetime dimensions. We also have derived the sub-subleading soft photon theorem analyzing two loop amplitudes in scalar QED. Finally, we conjectured the structure of leading non-analytic contribution to (sub)n-leading classical soft photon and graviton theorems which behave like u−n(ln u)n−1 for early and late retarded time u.

scholarly journals An algorithm for using deep learning convolutional neural networks with three dimensional depth sensor imaging in scoliosis detection

2021 ◽  
Author(s):  
Terufumi Kokabu ◽  
Satoshi Kanai ◽  
Noriaki Kawakami ◽  
Koki Uno ◽  
Toshiaki Kotani ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Three Dimensional ◽  
Depth Sensor
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