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. 2017 Jan 5;17(1):92.
doi: 10.3390/s17010092.

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

Ying He  1 , Bin Liang  2   3 , Yu Zou  4 , Jin He  5 , Jun Yang  6
Affiliations

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

Ying He et al. Sensors (Basel). .

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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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Development history of ToF cameras.
Figure 2
Figure 2
Principle of ToF cameras.
Figure 3
Figure 3
Experimental scene. (a) Experimental scene; (b) Camera bracket.
Figure 4
Figure 4
Influence of lighting on depth errors.
Figure 5
Figure 5
Influence of color on depth errors.
Figure 6
Figure 6
Four boards made of different materials.
Figure 7
Figure 7
Depth data of two sensors.
Figure 8
Figure 8
The measured cone.
Figure 9
Figure 9
Measurement errors of the cone.
Figure 10
Figure 10
Complex scene.
Figure 11
Figure 11
Depth images based on the point cloud of depth sensors.
Figure 12
Figure 12
Process of PF-SVM algorithm.
Figure 13
Figure 13
Depth error and error model.
Figure 14
Figure 14
Depth error correction results.
Figure 15
Figure 15
Depth error correction results of various colors and error model.
Figure 16
Figure 16
Depth error correction results and error model.
See this image and copyright information in PMC

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