Laser Radar Range Images Research Articles

Fractional-Order Total Variation Geiger-Mode Avalanche Photodiode Lidar Range-Image Denoising Algorithm Based on Spatial Kernel Function and Range Kernel Function

A Geiger-mode avalanche photodiode (GM-APD) laser radar range image has much noise when the signal-to-background ratios (SBRs) are low, making it difficult to recover the real target scene. In this paper, based on the GM-APD lidar denoising model of fractional-order total variation (FOTV), the spatial relationship and similarity relationship between pixels are obtained by using a spatial kernel function and range kernel function to optimize the fractional differential operator, and a new FOTV GM-APD lidar range-image denoising algorithm is designed. The lost information and range anomalous noise are suppressed while the target details and contour information are preserved. The Monte Carlo simulation and experimental results show that, under the same SBRs and statistical frame number, the proposed algorithm improves the target restoration degree by at least 5.11% and the peak signal-to-noise ratio (PSNR) by at least 24.6%. The proposed approach can accomplish the denoising of GM-APD lidar range images when SBRs are low.

Registration of partially overlapping laser-radar range images

To register partially overlapping three-dimensional point sets from different viewpoints, it is necessary to remove spurious corresponding point pairs that are not located in overlapping regions. Most variants of the iterative closest point (ICP) algorithm require users to manually select the rejection parameters for discarding spurious point pairs between the registering views. This requirement often results in unreliable and inaccurate registration. To overcome this problem, we present an improved ICP algorithm that can automatically determine the rejection percentage to reliably and accurately align partially overlapping laser-radar (ladar) range images. The similarity of k neighboring features of each nonplanar point is employed to determine reasonable point pairs in nonplanar regions, and the distance measurement method is used to find reasonable point pairs in planar regions. The rejection percentage can be obtained from these two sets of reasonable pairs. The performance of our algorithm is compared with that of five other algorithms using various models with low and high curvatures. The experimental results show that our algorithm is more accurate and robust than the other algorithms. © 2015

Fully automatic registration of structured objects based on laser radar range images

3D data registration is important in object modeling, and it is usually performed in two steps: coarse registration and fine registration. In order to automatically align laser radar (Ladar) range images of structured objects and avoid any feature detection or manual rough alignment, a 3DPEICP (iterative closest point based on object 3D pose estimation) registration method is proposed. Focusing on the coarse registration, the transformation parameters required for roughly aligning the 3D datasets are acquired by the 3D pose mainly estimated with the normals of large horizontal top surfaces and vertical sides of the targets. Fine registration is completed through the Iterative Closest Point (ICP) method within a few iterations for improving alignment accuracy. Experimental results of different models demonstrate the validity of the approach.

Expectation-maximization algorithms for image processing using multiscale models and mean- field theory, with applications to laser radar range profiling and segmentation

We describe a new class of computationally efficient algo- rithms designed to solve incomplete-data problems frequently encoun- tered in image processing and computer vision. The basis of this frame- work is the marriage of the expectation-maximization (EM) procedure with two powerful methodologies. In particular, we have incorporated optimal multiscale estimators into the EM procedure to compute esti- mates and error statistics efficiently. In addition, mean-field theory (MFT) from statistical mechanics is incorporated into the EM procedure to help solve the computational problems that arise from our use of Markov random-field (MRF) modeling of the hidden data in the EM formulation. We have applied this algorithmic framework and shown that it is effective in solving a wide variety of image-processing and computer-vision prob- lems. We demonstrate the application of our algorithmic framework to solve the problem of simultaneous anomaly detection, segmentation, and object profile estimation for noisy and speckled laser radar range images. © 2001 Society of Photo-Optical Instrumentation Engineers.

LADAR target detection using morphological shared-weight neural networks

Morphological shared-weight neural networks (MSNN) combine the feature extraction capability of mathematical morphology with the function-mapping capability of neural networks in a single trainable architecture. The MSNN method has been previously demonstrated using a variety of imaging sensors, including TV, forward-looking infrared (FLIR) and synthetic aperture radar (SAR). In this paper, we provide experimental results with laser radar (LADAR). We present three sets of experiments. In the first set of experiments, we use the MSNN to detect different types of targets simultaneously. In the second set, we use the MSNN to detect only a particular type of target. In the third set, we test a novel scenario, referred to as the Sims scenario: we train the MSNN to recognize a particular type of target using very few examples. A detection rate of 86% with a reasonable number of false alarms was achieved in the first set of experiments and a detection rate of close to 100% with very few false alarms was achieved in the second and third sets of experiments. In all the experiments, a novel pre-processing method is used to create a pseudo-intensity images from the original LADAR range images.

Detecting objects in three-dimensional laser radar range images

Statistical detection theory is used to develop the structure and performance of quasioptimal detection processors for 3-D coherent laser radar range imagery. Generalized likelihood-ratio tests (GLRTs) and receiver operating characteristics (ROCs) are presented for a detection scenario involving a variety of unknown object and background parameters. A computationally efficient, hard-limiter matched-filter processor is shown to yield performance closely approximating that of the GLRT.

Terrain segmentation using laser radar range data

A novel approach to segmentation of laser radar range images is presented. The approach is based on modeling horizontal and vertical scans of the terrain as piecewise-constant or piecewise-linear functions. The approach uses adaptive estimation based on Kalman filtering techniques. The performance of the segmentation algorithm is evaluated by application to laser range measurements. We also discuss how the output from the segmentation algorithm can be used for, e.g., object detection.