Sensor fusion of camera, GPS and IMU using fuzzy adaptive multiple motion models

A tracking system that will be used for augmented reality applications has two main requirements: accuracy and frame rate. The first requirement is related to the performance of the pose estimation algorithm and how accurately the tracking system can find the position and orientation of the user in the environment. Accuracy problems of current tracking devices, considering that they are low-cost devices, cause static errors during this motion estimation process. The second requirement is related to dynamic errors (the end-to-end system delay, occurring because of the delay in estimating the motion of the user and displaying images based on this estimate). This paper investigates combining the vision-based estimates with measurements from other sensors, GPS and IMU, in order to improve the tracking accuracy in outdoor environments. The idea of using Fuzzy Adaptive Multiple Models was investigated using a novel fuzzy rule-based approach to decide on the model that results in improved accuracy and faster convergence for the fusion filter. Results show that the developed tracking system is more accurate than a conventional GPS–IMU fusion approach due to additional estimates from a camera and fuzzy motion models. The paper also presents an application in cultural heritage context running at modest frame rates due to the design of the fusion algorithm

Multiple structure recovery with maximum coverage

We present a general framework for geometric model fitting based on a set coverage formulation that caters for intersecting structures and outliers in a simple and principled manner. The multi-model fitting problem is formulated in terms of the optimization of a consensus-based global cost function, which allows to sidestep the pitfalls of preference approaches based on clustering and to avoid the difficult trade-off between data fidelity and complexity of other optimization formulations. Two especially appealing characteristics of this method are the ease with which it can be implemented and its modularity with respect to the solver and to the sampling strategy. Few intelligible parameters need to be set and tuned, namely the inlier threshold and the number of desired models. The summary of the experiments is that our method compares favourably with its competitors overall, and it is always either the best performer or almost on par with the best performer in specific scenarios

Multiframe Motion Segmentation via Penalized MAP Estimation and Linear Programming

Motion segmentation is an important topic in computer vision. In this paper, we study the problem of multi-body motion segmentation under the affine camera model. We use a mixture of subspace model to describe the multi-body motions. Then the motion segmentation problem is formulated as an MAP estimation problem with model complexity penalty. With several candidate motion models, the problem can be naturally converted into a linear programming problem, which guarantees a global optimality. The main advantages of our algorithm include: It needs no priori on the number of motions and it has comparable high segmentation accuracy with the best of motion-number-known algorithms. Experiments on benchmark data sets illustrate these points

Toward Automated Aerial Refueling: Relative Navigation with Structure from Motion

The USAF\u27s use of UAS has expanded from reconnaissance to hunter/killer missions. As the UAS mission further expands into aerial combat, better performance and larger payloads will have a negative correlation with range and loiter times. Additionally, the Air Force Future Operating Concept calls for \formations of uninhabited refueling aircraft...[that] enable refueling operations partway inside threat areas. However, a lack of accurate relative positioning information prevents the ability to safely maintain close formation flight and contact between a tanker and a UAS. The inclusion of cutting edge vision systems on present refueling platforms may provide the information necessary to support a AAR mission by estimating the position of a trailing aircraft to provide inputs to a UAS controller capable of maintaining a given position. This research examines the ability of SfM to generate relative navigation information. Previous AAR research efforts involved the use of differential GPS, LiDAR, and vision systems. This research aims to leverage current and future imaging technology to compliment these solutions. The algorithm used in this thesis generates a point cloud by determining 3D structure from a sequence of 2D images. The algorithm then utilizes PCA to register the point cloud to a reference model. The algorithm was tested in a real world environment using a 1:7 scale F-15 model. Additionally, this thesis studies common 3D rigid registration algorithms in an effort characterize their performance in the AAR domain. Three algorithms are tested for runtime and registration accuracy with four data sets

Sampling and Subspace Methods for Learning Sparse Group Structures in Computer Vision

The unprecedented growth of data in volume and dimension has led to an increased number of computationally-demanding and data-driven decision-making methods in many disciplines, such as computer vision, genomics, finance, etc. Research on big data aims to understand and describe trends in massive volumes of high-dimensional data. High volume and dimension are the determining factors in both computational and time complexity of algorithms. The challenge grows when the data are formed of the union of group-structures of different dimensions embedded in a high-dimensional ambient space. To address the problem of high volume, we propose a sampling method referred to as the Sparse Withdrawal of Inliers in a First Trial (SWIFT), which determines the smallest sample size in one grab so that all group-structures are adequately represented and discovered with high probability. The key features of SWIFT are: (i) sparsity, which is independent of the population size; (ii) no prior knowledge of the distribution of data, or the number of underlying group-structures; and (iii) robustness in the presence of an overwhelming number of outliers. We report a comprehensive study of the proposed sampling method in terms of accuracy, functionality, and effectiveness in reducing the computational cost in various applications of computer vision. In the second part of this dissertation, we study dimensionality reduction for multi-structural data. We propose a probabilistic subspace clustering method that unifies soft- and hard-clustering in a single framework. This is achieved by introducing a delayed association of uncertain points to subspaces of lower dimensions based on a confidence measure. Delayed association yields higher accuracy in clustering subspaces that have ambiguities, i.e. due to intersections and high-level of outliers/noise, and hence leads to more accurate self-representation of underlying subspaces. Altogether, this dissertation addresses the key theoretical and practically issues of size and dimension in big data analysis

Two-view multibody structure-and-motion with outliers through model selection

Multibody structure-and-motion (MSaM) is the problem to establish the multiple-view geometry of several views of a 3D scene taken at different times, where the scene consists of multiple rigid objects moving relative to each other. We examine the case of two views. The setting is the following: Given are a set of corresponding image points in two images, which originate from an unknown number of moving scene objects, each giving rise to a motion model. Furthermore, the measurement noise is unknown, and there are a number of gross errors, which are outliers to all models. The task is to find an optimal set of motion models for the measurements. It is solved through Monte-Carlo sampling, careful statistical analysis of the sampled set of motion models, and simultaneous selection of multiple motion models to best explain the measurements. The framework is not restricted to any particular model selection mechanism because it is developed from a Bayesian viewpoint: Different model selection criteria are seen as different priors for the set of moving objects, which allow one to bias the selection procedure for different purposes.Konrad Schindler and David Suterhttp://www.computer.org/portal/web/csdl/doi/10.1109/TPAMI.2006.13

EXPLOITING LOW-DIMENSIONAL STRUCTURES IN MOTION PROBLEMS.

Ph.DDOCTOR OF PHILOSOPH