Engineering topics
Publications and source records attributed to Defigueiredo, Rui J. P..
A Generalized Image Point Correspondence (GIPC) algorithm, which enables the determination of 3-D motion parameters of an object in a configuration where both the object and the camera are moving, is discussed. A detailed error analysis of this algorithm has been carried out. Furthermore, the algorithm was tested on both simulated and video-acquired data, and its accuracy was determined.
Method and apparatus for fusion of data from optical and radar sensors by error minimization procedure is presented. The method was applied to the problem of shape reconstruction of an unknown surface at a distance. The method involves deriving an incomplete surface model from an optical sensor. The unknown characteristics of the surface are represented by some parameter. The correct value of the parameter is computed by iteratively generating theoretical predictions of the radar cross sections (RCS) of the surface, comparing the predicted and the observed values for the RCS, and improving the surface model from results of the comparison. Theoretical RCS may be computed from the surface model in several ways. One RCS prediction technique is the method of moments. The method of moments can be applied to an unknown surface only if some shape information is available from an independent source. The optical image provides the independent information.
The goal of the project was to develop a methodology for fusion of a Laser Range Imaging Device (LRID) and camera data. Our initial work in the project led to the conclusion that none of the LRID's that were available were sufficiently adequate for this purpose. Thus we spent the time and effort on the development of the new LRID with several novel features which elicit the desired fusion objectives. In what follows, we describe the device developed and built under contract. The Laser Range Imaging Device (LRID) is an instrument which scans a scene using a laser and returns range and reflection intensity data. Such a system would be extremely useful in scene analysis in industry and space applications. The LRID will be eventually implemented on board a mobile robot. The current system has several advantages over some commercially available systems. One improvement is the use of X-Y galvonometer scanning mirrors instead of polygonal mirrors present in some systems. The advantage of the X-Y scanning mirrors is that the mirror system can be programmed to provide adjustable scanning regions. For each mirror there are two controls accessible by the computer. The first is the mirror position and the second is a zoom factor which modifies the amplitude of the position of the parameter. Another advantage of the LRID is the use of a visible low power laser. Some of the commercial systems use a higher intensity invisible laser which causes safety concerns. By using a low power visible laser, not only can one see the beam and avoid direct eye contact, but also the lower intensity reduces the risk of damage to the eye, and no protective eyeware is required.
A theory of photometric stereo is proposed for a large class of non-Lambertian reflectance maps. The authors review the different reflectance maps proposed in the literature for modeling reflection from real-world surfaces. From this, they obtain a mathematical class of reflectance maps to which the maps belong. They show that three lights can be sufficient for a unique inversion of the photometric stereo equation for the entire class of reflectance maps. They also obtain a constraint on the positions of light sources for obtaining this solution. They investigate the sufficiency of three light sources to estimate the surface normal and the illuminant strength. The issue of completeness of reconstruction is addressed. They shown that if k lights are sufficient for a unique inversion, 2k lights are necessary for a complete inversion.
Recent work concerned with real-time machine vision is briefly reviewed. This work includes methodologies and techniques for optimal illumination, shape-from-shading of general (non-Lambertian) 3D surfaces, laser vision devices and technology, high level vision, sensor fusion, real-time computing, artificial neural network design and use, and motion estimation. Two new methods that are currently being developed for object recognition in clutter and for 3D attitude tracking based on line correspondence are discussed.
Conceptual robot-vision system processes data from both microwave and visible-wavelength sensors. Concept, called "fusion of sensors," yields better information on shapes of some objects than obtained from one type of sensor alone. Proposed system fuses data from television images with polarized low-resolution radar-scattering cross sections. Using iterative procedure, generates successive approximations to shape of target by minimizing differences between observed radar returns and radar returns observed if object characterized by computed scattering transfer matrix.
The vacuum of space presents special problems for optical image sensors. Metallic objects in this environment can produce intense specular reflections and deep shadows. By combining the polarized RCS with an incomplete camera image, it has become possible to better determine the shape of some simple three-dimensional objects. The radar data are used in an iterative procedure that generates successive approximations to the target shape by minimizing the error between computed scattering cross-sections and the observed radar returns. Favorable results have been obtained for simulations and experiments reconstructing plates, ellipsoids, and arbitrary surfaces.
Returned radar power estimates are used in an iterative procedure which generates successive approximations to the target shape in order to determine the shape of a 3-D surface. A simulation is shown which involves the reconstruction of an edge of a flat plate. Although this is a somewhat artificial example, it addresses the real problem of recovering edges of space objects lost in shadow or against a dark background. The results indicate that a microwave/optical sensor fusion system is possible, given sufficient computing power and accurate radar cross section measuring systems.
The thrust of automation and robotics for space applications has been proposed for increased productivity, improved reliability, increased flexibility, higher safety, and for the performance of automating time-consuming tasks, increasing productivity/performance of crew-accomplished tasks, and performing tasks beyond the capability of the crew. This paper provides a review of efforts currently in progress in the area of robotic vision. Both systems and algorithms are discussed. The evolution of future vision/sensing is projected to include the fusion of multisensors ranging from microwave to optical with multimode capability to include position, attitude, recognition, and motion parameters. The key feature of the overall system design will be small size and weight, fast signal processing, robust algorithms, and accurate parameter determination. These aspects of vision/sensing are also discussed.
A new system for the fusion of optical image data and polarized radar scattering cross-sections is presented. By considering the scattering data in conjunction with image data, the problem of ambiguity can be reduced. Only a small part of the surface needs to be reconstructed from the radar cross-sections; the remaining portion is constrained by the optical image.
This paper reports an extension of the MIAG algorithm for recognition and motion parameter determination of general 3-D polyhedral objects based on model matching techniques and using movement invariants as features of object representation. Results of tests conducted on the algorithm under conditions simulating space conditions are presented.