A robotic structured light camera

Olaya, Emerson J.; Berry, François; Mezouar, Youcef

doi:10.1109/aim.2014.6878165

Cited by 7 publications

(3 citation statements)

References 38 publications

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“…Several other applications including dental scanning [ 52 ], underwater surface profiling [ 53 ], and eye on hand for modular manipulator [ 54 , 55 , 56 ] have also been reported in the literature. An IR based adaptive baseline based camera was designed for a robotic arm to capture objects at different distances [ 57 ]. One of the major advantages of the structured light 3D camera systems is high-speed precise measurement in the industrial environment where the stereovision system cannot perform well due to poor illumination conditions.…”

Section: Structured Light 3d Cameramentioning

confidence: 99%

“…High scan speed structured light 3D cameras are required for the inspection of moving objects [ 58 ], e.g., to inspect car parts on the assembly line [ 57 ], which becomes difficult due to the movement of the assembly line and the vibration in the hanging part. The high scan speed of the structured light 3D camera system can achieve this through projecting patterns with high projection speed [ 12 ], reducing the number of patterns [ 40 ], using color-coded patterns [ 41 ], and performing the decoding process on the Graphic Processing Unit (GPU) [ 58 ].…”

Section: Structured Light 3d Cameramentioning

confidence: 99%

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FPGA Based Adaptive Rate and Manifold Pattern Projection for Structured Light 3D Camera System

Atif¹,

Lee²

2018

Sensors

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The quality of the captured point cloud and the scanning speed of a structured light 3D camera system depend upon their capability of handling the object surface of a large reflectance variation in the trade-off of the required number of patterns to be projected. In this paper, we propose and implement a flexible embedded framework that is capable of triggering the camera single or multiple times for capturing single or multiple projections within a single camera exposure setting. This allows the 3D camera system to synchronize the camera and projector even for miss-matched frame rates such that the system is capable of projecting different types of patterns for different scan speed applications. This makes the system capturing a high quality of 3D point cloud even for the surface of a large reflectance variation while achieving a high scan speed. The proposed framework is implemented on the Field Programmable Gate Array (FPGA), where the camera trigger is adaptively generated in such a way that the position and the number of triggers are automatically determined according to camera exposure settings. In other words, the projection frequency is adaptive to different scanning applications without altering the architecture. In addition, the proposed framework is unique as it does not require any external memory for storage because pattern pixels are generated in real-time, which minimizes the complexity and size of the application-specific integrated circuit (ASIC) design and implementation.

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Section: Structured Light 3d Cameramentioning

confidence: 99%

Section: Structured Light 3d Cameramentioning

confidence: 99%

FPGA Based Adaptive Rate and Manifold Pattern Projection for Structured Light 3D Camera System

Atif¹,

Lee²

2018

Sensors

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show abstract

“…Some methods use sensors, e.g., laser radar [4] or structured light cameras [5], to measure depth directly. These methods are expensive and are highly dependent on the environment.…”

Section: Introductionmentioning

confidence: 99%

A Unified Framework for Depth Prediction from a Single Image and Binocular Stereo Matching

et al. 2020

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Depth information has long been an important issue in computer vision. The methods for this can be categorized into (1) depth prediction from a single image and (2) binocular stereo matching. However, these two methods are generally regarded as separate tasks, which are accomplished in different network architectures when using deep learning-based methods. This study argues that these two tasks can be achieved using only one network with the same weights. We modify existing networks for stereo matching to perform the two tasks. We first enable the network capable of accepting both a single image and an image pair by duplicating the left image when the right image is absent. Then, we introduce a training procedure that alternatively selects training samples of depth prediction from a single image and binocular stereo matching. In this manner, the trained network can perform both tasks and single-image depth prediction even benefits from stereo matching to achieve better performance. Experimental results on KITTI raw dataset show that our model achieves state-of-the-art performances for accomplishing depth prediction from a single image and binocular stereo matching in the same architecture.

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Projector and Camera Shade Detection Based on Stripe Pattern Sequence for High Quality Structured Light Depth Imaging

Lee

Atif

2021

IEEE Access

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Structured light depth imaging offers a high precision of 3D measurement for applications to industrial modeling, inspection, reproduction and archiving. Despite recent progress, however, structured light depth imaging has yet to settle the trade-off between the number of captured 3D points and that of unwanted outliers, in particular, when a low reflectance surface is mixed with projector and camera shades. This paper presents an approach to solving such trade-off by accurately mapping projector and camera shades under the presence of a low reflectance surface. Unlike conventional approaches relying mostly on pattern intensity for detecting shades, the proposed approach first identifies stripe boundaries legitimate in terms of the projected pattern sequence. Based on the list of legitimate boundaries identified, then, the gaps in pixel and boundary addresses are analyzed to define the respective projector and camera shades, possibly, in the presence of address juxtaposition. Experimental results show that the proposed approach shows superior performance in shade detection as well as outlier removal compared to the state-of-the-art approaches, maximizing the number of captured 3D points while minimizing that of unwanted outliers.

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A robotic structured light camera

Cited by 7 publications

References 38 publications

FPGA Based Adaptive Rate and Manifold Pattern Projection for Structured Light 3D Camera System

FPGA Based Adaptive Rate and Manifold Pattern Projection for Structured Light 3D Camera System

A Unified Framework for Depth Prediction from a Single Image and Binocular Stereo Matching

Projector and Camera Shade Detection Based on Stripe Pattern Sequence for High Quality Structured Light Depth Imaging

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