We present a 3-D range map acquisition system using a gray-encoded time-multiplexing structured pattern. In this method the only information needed to reconstruct 3-D range map is whether the pixel is bright or not for the exposed structured patterns. A dedicated image sensor to capture the pattern consists of pixel parallel 1-bit A/D converter, in-pixel pattern address memory and column parallel digital pattern address readout circuit. This in-pixel memory and digital bit-parallel pattern address readout eliminate unnecessary readout of pattern data to enhance 3-D acquisition speed. We fabricated the image sensor in 0.18 µm CMOS and demonstrated up to 122 range map per second 3-D range map acquisition performance for 7 patterns with the average error of 3.2 mm under the condition of 10% pattern recognition error.

Structured light vision systems are based on the fact that the pixel location of light in an image has a unique association with the object depth. However, their applications are mainly limited to the properties of the object surface and the lighting conditions. This paper presents a robust vision system for accurate acquisition of 3-D surface data based on optimal structured light. To achieve depth measurement for a dynamic scene, the data acquisition must be performed with only a single image. Our special arrangement makes the image of the light stripe remaining sharp while the background becomes blurred. Moreover, a dynamic programming approach is proposed to track the optimal path while the laser beam is invisible or divergent under extreme condition. The principle and necessary mathematics for implementing the algorithm are presented. The robustness of the system against uncalibrated errors is demonstrated.

For the measurement of the 3D surface of micro-solderballs in IC (Integrated Circuit) manufacturing inspection, a binary grating project lenses of high MTF (Modulation Transfer Function) with tilted project plane is designed in this paper. Using a combination of lenses and a tilted optical layout both on object and image plane, the wave-front aberrations are reduced and the nonlinear image distortion is corrected with nonlinearity compensation, This optical lens allows us to project the structured light pattern to the inspected objects efficiently for clear deformed coded imaging, it could be used to online measure 3D shape of micro-solderballs with high precision and accuracy.

This paper introduces the hardware platform of the structured light processing based on depth imaging to perform a 3D modeling of cluttered workspace for home service robots. We have discovered that the degradation of precision and robustness comes mainly from the overlapping of multiple codes in the signal received at a camera pixel. Considering the criticality of separating the overlapped codes to precision and robustness, we proposed a novel signal separation code, referred to here as "Hierarchically Orthogonal Code (HOC)," for depth imaging. The proposed HOC algorithm was implemented by using hardware platform which applies the Xilinx XC2V6000 FPGA to perform a real time 3D modeling and the invisible IR (Infrared) pattern lights to eliminate any inconveniences for the home environment. The experimental results have shown that the proposed HOC algorithm significantly enhances the robustness and precision in depth imaging, compared to the best known conventional approaches. Furthermore, after we processed the HOC algorithm implemented on our hardware platform, the results showed that it required 34 ms of time to generate one 3D image. This processing time is about 24 times faster than the same implementation of HOC algorithm using software, and the real-time processing is realized.