Compressive laser ranging

Babbitt, W.R.; Barber, Zeb W.; Renner, C.

doi:10.1364/ol.36.004794

Cited by 8 publications

(4 citation statements)

References 9 publications

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“…CS techniques have previously designed to use prior knowledge to determine original signals from significantly reduced measurements compared with conventional sampling [26]- [28]. To be more specific, the goal of CS is to recover the signal vector x ∈ R n from the measurements vector…”

Section: Compressive Sensingmentioning

confidence: 99%

Compressive Sensing-Based Three-Dimensional Laser Imaging With Dual Illumination

Zhang

Guo

et al. 2019

IEEE Access

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Low-cost 3D imaging, particularly by using laser detection and ranging (LIDAR), is important for applications such as object recognition, surface mapping, and machine vision. Conventional time-offlight LIDAR uses a scanned laser to obtain the intensity and range of targets, which requires a narrow bandwidth of illumination and high-speed synchronizers. This paper presents a nonscanning prototype of a pulse-width-free 3D LIDAR which combines single-pixel imaging and diffractive optical elements, for the first time to our knowledge. Compressive sensing techniques are used to measure echo pulses from the target and reconstruct the intensity map of the target scene. Diffractive optical elements are also applied to generate structured illumination and the depth map of the target scene can be obtained from laser spot extraction. The simulation results are presented to verify the effectiveness of the proposed prototype as well as illustrate its superiority where traditional 3D imaging methods are unavailable or limited. This novel prototype has advantages of low cost and flexible structure at wavelengths beyond the visible spectrum and will be highly interesting for practical applications. INDEX TERMS Compressed sensing, image reconstruction, infrared imaging, laser radar.

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Section: Compressive Sensingmentioning

confidence: 99%

Compressive Sensing-Based Three-Dimensional Laser Imaging With Dual Illumination

Zhang

Guo

et al. 2019

IEEE Access

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“…CS can also be applied directly in the time-domain, and has been proposed for non-spatially resolving lidar systems [16]. In principle, one could combine this with transverse CS for a full 3D voxel reconstruction.…”

Section: Introductionmentioning

confidence: 99%

Photon counting compressive depth mapping

Howland¹,

Lum²,

Ware³

et al. 2013

Opt. Express

163

119

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We demonstrate a compressed sensing, photon counting lidar system based on the single-pixel camera. Our technique recovers both depth and intensity maps from a single under-sampled set of incoherent, linear projections of a scene of interest at ultra-low light levels around 0.5 picowatts. Only two-dimensional reconstructions are required to image a three-dimensional scene. We demonstrate intensity imaging and depth mapping at 256 × 256 pixel transverse resolution with acquisition times as short as 3 seconds. We also show novelty filtering, reconstructing only the difference between two instances of a scene. Finally, we acquire 32 × 32 pixel real-time video for three-dimensional object tracking at 14 frames-per-second.

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“…4 One method for performing computational imaging is to measure the multiplication of the image of a scene with a series of projection masks. The inner product of the image and one of the projection masks constitutes a single measurement.…”

Section: Introductionmentioning

confidence: 99%

Adaptive three-dimensional feature specific imaging

et al. 2014

Self Cite

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Abstract. Feature specific imaging is a computational imaging technique that minimizes the number of measurements needed to sufficiently reconstruct a scene by using a priori knowledge (e.g., the scene's second-order statistics) to judiciously, as well as possibly adaptively, choose the projection vectors to be measured. Here, we have developed an approach to three-dimensional adaptive feature specific imaging that takes into account the obstruction of distant objects by closer objects in the adaption of the projections and in the reconstruction algorithm. The developed system reconstructs the cross-range image of the scene at each range bin from a set of range resolved measurements from all the return from the scene at that range using only a single photodetector, while adapting to the obstruction of the scene by closer objects. Simulations and a proof-of-concept demonstration of adaptive three-dimensional feature specific imaging are presented.

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Compressive laser ranging

Cited by 8 publications

References 9 publications

Compressive Sensing-Based Three-Dimensional Laser Imaging With Dual Illumination

Compressive Sensing-Based Three-Dimensional Laser Imaging With Dual Illumination

Photon counting compressive depth mapping

Adaptive three-dimensional feature specific imaging

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