Sensors journal publishes an open-access paper "Depth Errors Analysis and Correction for Time-of-Flight (ToF) Cameras" by Ying He, Bin Liang, Yu Zou, Jin He, and Jun Yang from Harbin Institute of Technology and Tsinghua University, China. From the abstract:
"This paper analyzes the influence of typical external distractions including material, color, distance, lighting, etc. on the depth error of ToF cameras. Our experiments indicated that factors such as lighting, color, material, and distance could cause different influences on the depth error of ToF cameras. However, since the forms of errors are uncertain, it’s difficult to summarize them in a unified law. To further improve the measurement accuracy, this paper proposes an error correction method based on Particle Filter-Support Vector Machine (PF-SVM). Moreover, the experiment results showed that this method can effectively reduce the depth error of ToF cameras to 4.6 mm within its full measurement range (0.5–5 m)."
The authors use Mesa/Heptagon/AMS SR-4000 camera to get their experimental data:
"This paper analyzes the influence of typical external distractions including material, color, distance, lighting, etc. on the depth error of ToF cameras. Our experiments indicated that factors such as lighting, color, material, and distance could cause different influences on the depth error of ToF cameras. However, since the forms of errors are uncertain, it’s difficult to summarize them in a unified law. To further improve the measurement accuracy, this paper proposes an error correction method based on Particle Filter-Support Vector Machine (PF-SVM). Moreover, the experiment results showed that this method can effectively reduce the depth error of ToF cameras to 4.6 mm within its full measurement range (0.5–5 m)."
The authors use Mesa/Heptagon/AMS SR-4000 camera to get their experimental data:
ToF Camera Error Analysis and Correction
Reviewed by MCH
on
January 07, 2017
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Reviewed by MCH
on
January 07, 2017
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