Noisy Range Research Articles

Autonomous construction of three-dimensional models from range data

This paper presents a method for autonomously constructing a complete general purpose surface description (model) of an object. The models are constructed based on a large number of noisy range images from an essentially arbitrary set of viewpoints around the object. The resulting models consist of a set of parametric surface patches. Results of testing the model building algorithm on two synthetic test objects are also presented.

Fractal Modeling of Natural Terrain: Analysis and Surface Reconstruction with Range Data

In this paper we address two issues in modeling natural terrain using fractal geometry: estimation of fractal dimension, and fractal surface reconstruction. For estimation of fractal dimension, we extend the fractal Brownian function approach to accommodate irregularly sampled data, and we develop methods for segmenting sets of points exhibiting self-similarity over only certain scales. For fractal surface reconstruction, we extend Szeliski's regularization with fractal priors method to use a temperature parameter that depends on fractal dimension. We demonstrate both estimation and reconstruction with noisy range imagery of natural terrain.

Second-order implicit polynomials for segmentation of range images

In this paper, we present an algorithm for segmentation and feature extraction for noisy range images. We use the edge- and region-based approach for obtaining reliable segmentation maps. For edge detection, regions having much higher values of planar fit error are assumed to contain edges. These edge regions are explored for detection of crease edges, which can be located at the maxima of the curvature. However, due to noise, the maximas are generated at a number of points other than the actual edges. These extraneous curvature maximas have a substantially smaller value of curvature compared with the curvature of the actual edges. The true edges are detected using an approach based on local thresholding within edge regions. The detected edges segment the range image into smooth patches. On each of the smooth patches, a planar/quadric fit is obtained using a novel non-iterative procedure. To obtain reliable fits, we had to restrict the permissible class of quadrics to surfaces of revolution. The quadric fit obtained gives a very reliable estimate of the position and orientation of the axis for curved surfaces. The approach has been demonstrated on real range data and has been found to give good results.

Discontinuity-preserving and viewpoint invariant reconstruction of visible surfaces using a first order regularization

This paper describes the application of a first order regularization technique to the reconstruction of visible surfaces. Our approach is a computationally efficient first order method that simultaneously achieves approximate invariance and preservation of discontinuities. It is also robust with respect to the smoothing parameter /spl lambda/. The robustness property to /spl lambda/ allows a free choice of /spl lambda/ without struggling to determine an optimal /spl lambda/ that provides the best reconstruction. A new approximately invariant first order stabilizing function for surface reconstruction is obtained by employing a first order Taylor expansion of a nonconvex invariant stabilizing function that is expanded at the estimated value of the squared gradient instead of at zero. The data compatibility measure is the squared perpendicular distance between the reconstructed surface and the constraint surface. This combination of stabilizing function and data compatibility measure is necessary to achieve invariance with respect to rotations and translations. Sharp preservation of discontinuities is achieved by a weighted sum of adjacent pixels. The results indicate that the proposed methods perform well on sparse noisy range data. In addition, the volume between two surfaces normalized by the surface area (interpreted as average distance between two surfaces) is proposed as an invariant measure for the comparison of reconstruction results.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Recovering scene structures from scattered surface points

Given scattered points from range or volumetric images, methods for the recovery of scene structures are described. The structures are represented by rational Gaussian surfaces. Examples demonstrating reconstruction of three-dimensional (3D) scenes from scattered depth measurements obtained from stereo disparity, recovery of 3D shapes from noisy range data, and segmentation of volumetric images for the extraction of generalized cylinders are presented.

Depth recovery using Markov Random Fields

This paper deals with the problem of depth recovery or surface reconstruction from sparse and noisy range data. The image is modelled as a Markov Random Field and a new potential function is developed to effectively detect discontinuities in highly sparse and noisy images. No use of any line process is made. Interpolation over missing data sites is first done using local characteristics and simulated annealing is then used to compute the maximum a posteriori (map) estimate. Results of software simulations carried out on actual range images along with details of the simulations are presented.

Systematic errors in ground referenced geometric height monitoring

This paper investigates the existence and treatment of systematic errors in geometric height measurement systems. A minimal system of one SSR aided by a low power omni radar equipped with an independent interrogator is taken for reference. Biases are identified to be created by the aircraft transponder and by the nonlinear processing of the available noisy radar range and bearing measurements. The first source is treated by augmenting the estimation process to include the transponder bias. The latter source creates an ‘inherent bias’ which cannot be estimated by augmenting the estimation process. The inherent bias is further analysed by determining the main factors affecting its magnitude. It is shown that the inherent bias limits the useful area in which the geometric height can be reliably estimated. Although the inherent bias investigation method is primarily derived for the SSR-omni measurement system, it can be applied in a straightforward manner to other geometric height monitoring systems employing nonlinear processing of the original measurements.

Model-based recognition and range imaging for a guided vehicle

Progress in providing the Oxford Autonomous Guided Vehicle (AGV) with range sensing and model-based recognition of classes of industrial artifacts is described. The GEC ‘Turtle’ vehicle is the basis for a sensor integration project in which vision sensors play the strategic role of locating pallets in an unstructured environment; we report on two key areas involved with achieving this goal. The design and geometry of a novel range sensor developed jointly with the National Engineering Laboratories is described. The problems of dealing with noisy range images are discussed, and a number of solutions proposed and tested. A system for pose recognition of generic models from range-imagery is described.

Segmentation of noisy range images using the small-scale planarity of man-made vehicles

Segmentation ofvehicles in natural backgrounds using absolute range imagery is considered. The segmentation technique exploits the small-scale planarity of man-made vehicles without explicit use of surface orientation information. A planarity test is used as the initial step in partitioning man-made vehicles from natural backgrounds. The planarity test is based on an estimate of the statistics of the absolute value of the error associated with fitting planes to small regions of the Cartesian coordinates of range image pixels. The planarity test is developed as a function of the standard deviation of the noise associated with range measurements. Heuristics used in conjunction with this algorithm are also presented. This algorithm has been evaluated on a database of 57 real range images, and results are presented. Application of the algorithm to two specific cases is shown in detail.

New filter for radar use and its microprocessor realization†

A new digital filter is presented for use in radar systems to extract smoothed range and range rate data from noisy range data. The performance of a real-time micro-processor realization of the filter is discussed, and comparisons are provided with fillers that have been previously reported.

Microprocessor realizations of range and range-rate filters in radar systems

This paper describes the implementation of digital radar range-rate filters on a microprocessor-based system. A range-rate filter processes a digitized noisy range signal to recover smoothed range data and its derivative, range rate. Two filter designs are implemented. Considerations aiding their efficient operation on an 8-bit microprocessor are discussed. The filters are subjected to a noisy range input signal of known variance, and the associated output signals are statistically analysed to determine noise-rejection characteristics. These results are compared to analytical predictions.