He Ying, Liang Bin, Zou Yu, He Jin, Yang Jun
Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen 518055, China.
Department of Automation, Tsinghua University, Beijing 100084, China.
Sensors (Basel). 2017 Jan 5;17(1):92. doi: 10.3390/s17010092.
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).
飞行时间（ToF）相机是近年来发展迅速的一项技术，它是一种3D成像传感器，能够以高帧率提供深度图像和幅度图像。由于ToF相机受成像条件和外部环境的限制，其采集的数据总是存在一定误差。本文分析了包括材质、颜色、距离、光照等典型外部干扰因素对ToF相机深度误差的影响。我们的实验表明，光照、颜色、材质和距离等因素会对ToF相机的深度误差产生不同的影响。然而，由于误差形式不确定，难以用统一的规律进行总结。为了进一步提高测量精度，本文提出了一种基于粒子滤波-支持向量机（PF-SVM）的误差校正方法。此外，实验结果表明，该方法能够在ToF相机的全测量范围（0.5 - 5米）内有效地将深度误差降低至4.6毫米。
