Eyesafe ladar testbed with coaxial color imager

Pack, Robert T.; Swasey, Jason; Fullmer, R. Rees; Budge, Scott E.; Israelsen, Paul; Petersen, Brad; Cook, Dean

doi:10.1117/12.818146

Cited by 7 publications

(7 citation statements)

References 2 publications

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“…11 As shown in Fig. 1, the ELT, mounted in the VISSTA van, has a single sensor flying spot laser scanner that sits inside a movable turret on top of the van.…”

Section: The Eyesafe Ladar Test-bedmentioning

confidence: 99%

Range resolution improvement of eyesafe ladar testbed (ELT) measurements using sparse signal deconvolution

Budge

Gunther

2014

Laser Radar Technology and Applications XIX; And Atmospheric Propagation XI

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The Eyesafe Ladar Test-bed (ELT) is a experimental ladar system with the capability of digitizing return laser pulse waveforms at 2 GHz. These waveforms can then be exploited off-line in the laboratory to develop signal processing techniques for noise reduction, range resolution improvement, and range discrimination between two surfaces of similar range interrogated by a single laser pulse. This paper presents the results of experiments with new deconvolution algorithms with the hoped for gains of improving the range discrimination of the ladar system. The sparsity of ladar returns is exploited to solve the deconvolution problem in two steps. The first step is to estimate a point target response using a database of measured calibration data. This basic target response is used to construct a dictionary of target responses with different delays/ranges. Using this dictionary ladar returns from a wide variety of surface configurations can be synthesized by taking linear combinations. A sparse linear combination matches the physical reality that ladar returns consist of the overlapping of only a few pulses. The dictionary construction process is a pre-processing step that is performed only once.The deconvolution step is performed by minimizing the error between the measured ladar return and the dictionary model while constraining the coefficient vector to be sparse. Other constraints such as the nonnegativity of the coefficients are also applied. The results of the proposed technique are presented in the paper and are shown to compare favorably with previously investigated deconvolution techniques.

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“…11 As shown in Fig. 1, the ELT, mounted in the VISSTA van, has a single sensor flying spot laser scanner that sits inside a movable turret on top of the van.…”

Section: The Eyesafe Ladar Test-bedmentioning

confidence: 99%

Range resolution improvement of eyesafe ladar testbed (ELT) measurements using sparse signal deconvolution

Budge

Gunther

2014

Laser Radar Technology and Applications XIX; And Atmospheric Propagation XI

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“…The combination of a flash or time-of-flight lidar with a digital (electro-optic or EO) camera that produces data that is fused at the subpixel level is known as a "texel camera. [28][29][30] " An example of a compact texel camera prototype is shown in Fig. 1, with sensor specifications given in Table 1.…”

Section: Texel Cameramentioning

confidence: 99%

Automatic registration of fused lidar/digital imagery (texel images) for three-dimensional image creation

2014

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Several photogrammetry-based methods have been proposed that the derive three-dimensional (3-D) information from digital images from different perspectives, and lidar-based methods have been proposed that merge lidar point clouds and texture the merged point clouds with digital imagery. Image registration alone has difficulty with smooth regions with low contrast, whereas point cloud merging alone has difficulty with outliers and a lack of proper convergence in the merging process. This paper presents a method to create 3-D images that uses the unique properties of texel images (pixel-fused lidar and digital imagery) to improve the quality and robustness of fused 3-D images. The proposed method uses both image processing and point-cloud merging to combine texel images in an iterative technique. Since the digital image pixels and the lidar 3-D points are fused at the sensor level, more accurate 3-D images are generated because registration of image data automatically improves the merging of the point clouds, and vice versa. Examples illustrate the value of this method over other methods. The proposed method also includes modifications for the situation where an estimate of position and attitude of the sensor is known, when obtained from low-cost global positioning systems and inertial measurement units sensors.

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“…There is no analytical solution for (x n , y n ), though algorithms for approximating the solution have been proposed. 15,17 Following the approach of Melen, 17 Algorithm 1 can be used, where δ(p n ) = d r p n + d t − p n , and d r and d t are given in (4) and 5, respectively. Note that the solution for p n is fixed for each pixel in the ladar array, and can be pre-computed and used in a table lookup.…”

Section: Calibrated 3d Pointsmentioning

confidence: 99%

“…If the 3D information is fused to the digital image at the pixel level by the sensor, the image can thought of as wireframe mesh covered with texture information, or a "texel image." Examples of sensors developed to achieve this result include tripod-based, 3 mobile platform-based, 4 and handheld configurations. 5 There has also been several papers on Time-of-Flight (TOF) sensor calibration.…”

Section: Introductionmentioning

confidence: 99%

A calibration-and-error correction method for improved texel (fused ladar/digital camera) images

Budge

2012

SPIE Proceedings

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The fusion of imaging ladar information and digital imagery results in 2.5-D surfaces covered with texture information. Called "texel images," these datasets, when taken from different viewpoints, can be combined to create 3-D images of buildings, vehicles, or other objects. These 3-D images can then be further processed for automatic target recognition, or viewed in a 3-D viewer for tactical planning purposes. This paper presents a procedure for calibration, error correction, and fusing of ladar and digital camera information from a single hand-held sensor to create accurate texel images. A brief description of a prototype sensor is given, along with calibration technique used with the sensor, which is applicable to other imaging ladar/digital image sensor systems. The method combines systematic error correction of the ladar data, correction for lens distortion of the digital camera image, and fusion of the ladar to the camera data in a single process. The result is a texel image acquired directly from the sensor. Examples of the resulting images, with improvements from the proposed algorithm, are presented.

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Eyesafe ladar testbed with coaxial color imager

Cited by 7 publications

References 2 publications

Range resolution improvement of eyesafe ladar testbed (ELT) measurements using sparse signal deconvolution

Range resolution improvement of eyesafe ladar testbed (ELT) measurements using sparse signal deconvolution

Automatic registration of fused lidar/digital imagery (texel images) for three-dimensional image creation

A calibration-and-error correction method for improved texel (fused ladar/digital camera) images

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