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Sensors (Basel). 2017 Jan 5;17(1). pii: E92. doi: 10.3390/s17010092.

Depth Errors Analysis and Correction for Time-of-Flight (ToF) Cameras.

Author information

1
Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen 518055, China. heying@hitsz.edu.cn.
2
Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen 518055, China. bliang@tsinghua.edu.cn.
3
Department of Automation, Tsinghua University, Beijing 100084, China. bliang@tsinghua.edu.cn.
4
Department of Automation, Tsinghua University, Beijing 100084, China. y-zou10@mails.tsinghua.edu.cn.
5
Department of Automation, Tsinghua University, Beijing 100084, China. he-j15@mails.tsinghua.edu.cn.
6
Shenzhen Graduate School, Tsinghua University, Shenzhen 518055, China. yangjun603@mails.tsinghua.edu.cn.

Abstract

Time-of-Flight (ToF) cameras, a technology which has developed rapidly in recent years, are 3D imaging sensors providing a depth image as well as an amplitude image with a high frame rate. As a ToF camera is limited by the imaging conditions and external environment, its captured data are always subject to certain errors. 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).

KEYWORDS:

SVM; ToF camera; depth error; error correction; error modeling; particle filter

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