Browsing by Subject "Sensor Fusion"
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Item 3D Multi-Field Multi-Scale Features From Range Data In Spacecraft Proximity Operations(2012-07-16) Flewelling, Brien RoyA fundamental problem in spacecraft proximity operations is the determination of the 6 degree of freedom relative navigation solution between the observer reference frame and a reference frame tied to a proximal body. For the most unconstrained case, the proximal body may be uncontrolled, and the observer spacecraft has no a priori information on the body. A spacecraft in this scenario must simultaneously map the generally poorly known body being observed, and safely navigate relative to it. Simultaneous localization and mapping(SLAM)is a difficult problem which has been the focus of research in recent years. The most promising approaches extract local features in 2D or 3D measurements and track them in subsequent observations by means of matching a descriptor. These methods exist for both active sensors such as Light Detection and Ranging(LIDAR) or laser RADAR(LADAR), and passive sensors such as CCD and CMOS camera systems. This dissertation presents a method for fusing time of flight(ToF) range data inherent to scanning LIDAR systems with the passive light field measurements of optical systems, extracting features which exploit information from each sensor, and solving the unique SLAM problem inherent to spacecraft proximity operations. Scale Space analysis is extended to unstructured 3D point clouds by means of an approximation to the Laplace Beltrami operator which computes the scale space on a manifold embedded in 3D object space using Gaussian convolutions based on a geodesic distance weighting. The construction of the scale space is shown to be equivalent to both the application of the diffusion equation to the surface data, as well as the surface evolution process which results from mean curvature flow. Geometric features are localized in regions of high spatial curvature or large diffusion displacements at multiple scales. The extracted interest points are associated with a local multi-field descriptor constructed from measured data in the object space. Defining features in object space instead of image space is shown to bean important step making the simultaneous consideration of co-registered texture and the associated geometry possible. These descriptors known as Multi-Field Diffusion Flow Signatures encode the shape, and multi-texture information of local neighborhoods in textured range data. Multi-Field Diffusion Flow Signatures display utility in difficult space scenarios including high contrast and saturating lighting conditions, bland and repeating textures, as well as non-Lambertian surfaces. The effectiveness and utility of Multi-Field Multi-Scale(MFMS) Features described by Multi-Field Diffusion Flow Signatures is evaluated using real data from proximity operation experiments performed at the Land Air and Space Robotics(LASR) Laboratory at Texas A&M University.Item Nonlinear and distributed sensory estimation(Texas A&M University, 2005-08-29) Sugathevan, SuranthiranMethods to improve performance of sensors with regard to sensor nonlinearity, sensor noise and sensor bandwidths are investigated and new algorithms are developed. The necessity of the proposed research has evolved from the ever-increasing need for greater precision and improved reliability in sensor measurements. After describing the current state of the art of sensor related issues like nonlinearity and bandwidth, research goals are set to create a new trend on the usage of sensors. We begin the investigation with a detailed distortion analysis of nonlinear sensors. A need for efficient distortion compensation procedures is further justified by showing how a slight deviation from the linearity assumption leads to a very severe distortion in time and in frequency domains. It is argued that with a suitable distortion compensation technique the danger of having an infinite bandwidth nonlinear sensory operation, which is dictated by nonlinear distortion, can be avoided. Several distortion compensation techniques are developed and their performance is validated by simulation and experimental results. Like any other model-based technique, modeling errors or model uncertainty affects performance of the proposed scheme, this leads to the innovation of robust signal reconstruction. A treatment for this problem is given and a novel technique, which uses a nominal model instead of an accurate model and produces the results that are robust to model uncertainty, is developed. The means to attain a high operating bandwidth are developed by utilizing several low bandwidth pass-band sensors. It is pointed out that instead of using a single sensor to measure a high bandwidth signal, there are many advantages of using an array of several pass-band sensors. Having shown that employment of sensor arrays is an economic incentive and practical, several multi-sensor fusion schemes are developed to facilitate their implementation. Another aspect of this dissertation is to develop means to deal with outliers in sensor measurements. As fault sensor data detection is an essential element of multi-sensor network implementation, which is used to improve system reliability and robustness, several sensor scheduling configurations are derived to identify and to remove outliers.