Key Parameters for Performance and Resilience Modeling of 3D Time-of-Flight Cameras Under Consideration of Signal-to-Noise Ratio and Phase Noise Wiggling
Because of their resilience, Time-of-Flight (ToF) cameras are now essential components in scientific and industrial settings. This paper outlines the essential factors for modeling 3D ToF cameras, with specific emphasis on analyzing the phenomenon known as “wiggling”. Through our investigation, we d...
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| Format: | Article |
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MDPI AG
2024-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/1/109 |
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| author | Niklas Alexander Köhler Marcel Geis Claudius Nöh Alexandra Mielke Volker Groß Robert Lange Keywan Sohrabi Jochen Frey |
| author_facet | Niklas Alexander Köhler Marcel Geis Claudius Nöh Alexandra Mielke Volker Groß Robert Lange Keywan Sohrabi Jochen Frey |
| author_sort | Niklas Alexander Köhler |
| collection | DOAJ |
| description | Because of their resilience, Time-of-Flight (ToF) cameras are now essential components in scientific and industrial settings. This paper outlines the essential factors for modeling 3D ToF cameras, with specific emphasis on analyzing the phenomenon known as “wiggling”. Through our investigation, we demonstrate that wiggling not only causes systematic errors in distance measurements, but also introduces periodic fluctuations in statistical measurement uncertainty, which compounds the dependence on the signal-to-noise ratio (SNR). Armed with this knowledge, we developed a new 3D camera model, which we then made computationally tractable. To illustrate and evaluate the model, we compared measurement data with simulated data of the same scene. This allowed us to individually demonstrate various effects on the signal-to-noise ratio, reflectivity, and distance. |
| format | Article |
| id | doaj-art-1e3228fab25d4608bf03a3e4969af710 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-1e3228fab25d4608bf03a3e4969af7102025-01-10T13:20:54ZengMDPI AGSensors1424-82202024-12-0125110910.3390/s25010109Key Parameters for Performance and Resilience Modeling of 3D Time-of-Flight Cameras Under Consideration of Signal-to-Noise Ratio and Phase Noise WigglingNiklas Alexander Köhler0Marcel Geis1Claudius Nöh2Alexandra Mielke3Volker Groß4Robert Lange5Keywan Sohrabi6Jochen Frey7Department of Health, University of Applied Sciences Mittelhessen, 35390 Giessen, GermanyDepartment of Health, University of Applied Sciences Mittelhessen, 35390 Giessen, GermanyDepartment of Health, University of Applied Sciences Mittelhessen, 35390 Giessen, GermanyInstitute of Safety and Security Research (ISF), Hochschule Bonn-Rhein-Sieg, 53757 Sankt Augustin, GermanyDepartment of Health, University of Applied Sciences Mittelhessen, 35390 Giessen, GermanyInstitute of Safety and Security Research (ISF), Hochschule Bonn-Rhein-Sieg, 53757 Sankt Augustin, GermanyDepartment of Health, University of Applied Sciences Mittelhessen, 35390 Giessen, GermanyDepartment of Electrical Engineering and Information Technology, University of Applied Sciences Mittelhessen, 35390 Giessen, GermanyBecause of their resilience, Time-of-Flight (ToF) cameras are now essential components in scientific and industrial settings. This paper outlines the essential factors for modeling 3D ToF cameras, with specific emphasis on analyzing the phenomenon known as “wiggling”. Through our investigation, we demonstrate that wiggling not only causes systematic errors in distance measurements, but also introduces periodic fluctuations in statistical measurement uncertainty, which compounds the dependence on the signal-to-noise ratio (SNR). Armed with this knowledge, we developed a new 3D camera model, which we then made computationally tractable. To illustrate and evaluate the model, we compared measurement data with simulated data of the same scene. This allowed us to individually demonstrate various effects on the signal-to-noise ratio, reflectivity, and distance.https://www.mdpi.com/1424-8220/25/1/109Time-of-Flight (ToF)amplitude wigglingphase noise wigglingabsolute phase wigglingcamera modelingcamera characterization |
| spellingShingle | Niklas Alexander Köhler Marcel Geis Claudius Nöh Alexandra Mielke Volker Groß Robert Lange Keywan Sohrabi Jochen Frey Key Parameters for Performance and Resilience Modeling of 3D Time-of-Flight Cameras Under Consideration of Signal-to-Noise Ratio and Phase Noise Wiggling Sensors Time-of-Flight (ToF) amplitude wiggling phase noise wiggling absolute phase wiggling camera modeling camera characterization |
| title | Key Parameters for Performance and Resilience Modeling of 3D Time-of-Flight Cameras Under Consideration of Signal-to-Noise Ratio and Phase Noise Wiggling |
| title_full | Key Parameters for Performance and Resilience Modeling of 3D Time-of-Flight Cameras Under Consideration of Signal-to-Noise Ratio and Phase Noise Wiggling |
| title_fullStr | Key Parameters for Performance and Resilience Modeling of 3D Time-of-Flight Cameras Under Consideration of Signal-to-Noise Ratio and Phase Noise Wiggling |
| title_full_unstemmed | Key Parameters for Performance and Resilience Modeling of 3D Time-of-Flight Cameras Under Consideration of Signal-to-Noise Ratio and Phase Noise Wiggling |
| title_short | Key Parameters for Performance and Resilience Modeling of 3D Time-of-Flight Cameras Under Consideration of Signal-to-Noise Ratio and Phase Noise Wiggling |
| title_sort | key parameters for performance and resilience modeling of 3d time of flight cameras under consideration of signal to noise ratio and phase noise wiggling |
| topic | Time-of-Flight (ToF) amplitude wiggling phase noise wiggling absolute phase wiggling camera modeling camera characterization |
| url | https://www.mdpi.com/1424-8220/25/1/109 |
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