&lt;title&gt;3D flash ladar at Raytheon&lt;/title&gt;

Halmos, Maurice J.; Jack, Michael D.; Asbrock, James F.; Anderson, Charles D.; Bailey, Steven L.; Chapman, G. R.; Gordon, E.; Herning, P. E.; Kalisher, M. H.; Klaras, Louis F.; Kosai, K.; Liquori, V.; Pines, Mike; Randall, Valerie; Reeder, R. A.; Rosbeck, Joseph P.; Sen, Sanghamitra; Trotta, Patrick A.; Wetzel, Paul A.; Hunter, A. T.; Jensen, J. Eric; DeLyon, T. J.; Trussell, C. W.; Hutchinson, James A.; Balcerak, Raymond S.

doi:10.1117/12.440096

Cited by 6 publications

(4 citation statements)

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“…However, in contrast to that in [6], we do not associate each photon in the histogram with a particular component in a mixture model but treat the histogram as a discrete representation of a spatially heterogeneous Poisson process whose intensity is a linear superposition of the scaled and shifted templates of the form specified by (1). The transitions of the Markov chain we generate involved moves of various types.…”

Section: Rjmcmc Methodologymentioning

confidence: 99%

“…When the number of returns is increased, the approach chosen is that of implementing first/last pulse logic in the receiver electronics. In first pulse logic, this involves processing the first pulse detected, whereas in last pulse logic, this involves sampling and holding all the returns after the first but only recording the final return [1]. In practice, this fails because the pulse of interest is not obvious and more complex processing of the returns is needed.…”

Section: Background and Related Workmentioning

confidence: 99%

“…Normally, such systems process only an assumed single return from a single opaque surface. In recent years, interest has grown in situations in which the laser return consists of multiple peaks, for example, due to the footprint of the beam impinging on a target with surfaces distributed in depth or with semitransparent surfaces [1], [2], [3], [4]. The goal of our work is a complete characterization of the 3D surfaces viewed by the laser imaging system.…”

Section: Introductionmentioning

confidence: 99%

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Bayesian Analysis of Lidar Signals with Multiple Returns

Hernandez-Marin

Wallace

Gibson

2007

IEEE Trans. Pattern Anal. Mach. Intell.

129

118

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Abstract-Time-Correlated Single Photon Counting and Burst Illumination Laser data can be used for range profiling and target classification. In general, the problem is to analyze the response from a histogram of either photon counts or integrated intensities to assess the number, positions, and amplitudes of the reflected returns from object surfaces. The goal of our work is a complete characterization of the 3D surfaces viewed by the laser imaging system. The authors present a unified theory of pixel processing that is applicable to both approaches based on a Bayesian framework, which allows for careful and thorough treatment of all types of uncertainties associated with the data. We use reversible jump Markov chain Monte Carlo (RJMCMC) techniques to evaluate the posterior distribution of the parameters and to explore spaces with different dimensionality. Further, we use a delayed rejection step to allow the generated Markov chain to mix better through the use of different proposal distributions. The approach is demonstrated on simulated and real data, showing that the return parameters can be estimated to a high degree of accuracy. We also show some practical examples from both near and far-range depth imaging.

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Section: Rjmcmc Methodologymentioning

confidence: 99%

Section: Background and Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Bayesian Analysis of Lidar Signals with Multiple Returns

Hernandez-Marin

Wallace

Gibson

2007

IEEE Trans. Pattern Anal. Mach. Intell.

129

118

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show abstract

“…Early developments are described in [26] and the development of an eye-safe 128 × 128 pixel system is given in [27]. Developments at Raytheon and by Areté Associates are discussed in [28] and [29], respectively.…”

Section: Introductionmentioning

confidence: 99%

Use of 3D laser radar for navigation of unmanned aerial and ground vehicles in urban and indoor environments

2007

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This paper discusses the integration of Inertial measurements with measurements from a three-dimensional (3D) imaging sensor for position and attitude determination of unmanned aerial vehicles (UAV) and autonomous ground vehicles (AGV) in urban or indoor environments. To enable operation of UAVs and AGVs at any time in any environment a Precision Navigation, Attitude, and Time (PNAT) capability is required that is robust and not solely dependent on the Global Positioning System (GPS). In urban and indoor environments a GPS position capability may not only be unavailable due to shadowing, significant signal attenuation or multipath, but also due to intentional denial or deception. Although deep integration of GPS and Inertial Measurement Unit (IMU) data may prove to be a viable solution an alternative method is being discussed in this paper.The alternative solution is based on 3D imaging sensor technologies such as Flash Ladar (Laser Radar). Flash Ladar technology consists of a modulated laser emitter coupled with a focal plane array detector and the required optics. Like a conventional camera this sensor creates an "image" of the environment, but producing a 2D image where each pixel has associated intensity vales the flash Ladar generates an image where each pixel has an associated range and intensity value. Integration of flash Ladar with the attitude from the IMU allows creation of a 3-D scene. Current low-cost Flash Ladar technology is capable of greater than 100 x 100 pixel resolution with 5 mm depth resolution at a 30 Hz frame rate.The proposed algorithm first converts the 3D imaging sensor measurements to a point cloud of the 3D, next, significant environmental features such as planar features (walls), line features or point features (corners) are extracted and associated from one 3D imaging sensor frame to the next. Finally, characteristics of these features such as the normal or direction vectors are used to compute the platform position and attitude changes. These "delta" position and attitudes are then used calibrate the IMU. Note, that the IMU is not only required to form the point cloud of the environment expressed in the navigation frame, but also to perform association of the features from one flash Ladar frame to the next. This paper will discuss the performance of the proposed 3D imaging sensor feature extraction, position change estimator and attitude change estimator using both simulator data and data collected from a moving platform in an indoor environment. The former consists of data from a simulated IMU and flash Ladar installed on an aerial vehicle for various trajectories through an urban environment. The latter consists of measurements from a CSEM Swissranger 3D imaging sensor and a MicroStrain low-cost IMU. Data was collected on a manually operated aerial vehicle inside the Ohio University School of Electrical Engineering and Computer Science building.

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Flash Lidar Sensor Using Fiber-Coupled APDs

Zhou

Yang

et al. 2015

IEEE Sensors J.

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<title>3D flash ladar at Raytheon</title>

Cited by 6 publications

References 0 publications

Bayesian Analysis of Lidar Signals with Multiple Returns

Bayesian Analysis of Lidar Signals with Multiple Returns

Use of 3D laser radar for navigation of unmanned aerial and ground vehicles in urban and indoor environments

Flash Lidar Sensor Using Fiber-Coupled APDs

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