Abstract. Multiple rotation averaging is an important problem in computer vision. The problem is challenging because of the nonlinear constraints required to represent the set of rotations. To our knowledge no one has proposed any globally optimal solution for the case of simultaneous updates of the rotations. In this paper we propose a simple procedure based on Lagrangian duality that can be used to verify global optimality of a local solution, by solving a linear system of equations. We show experimentally on real and synthetic data that unless the noise levels are extremely high this procedure always generates the globally optimal solution.
Abstract. The embedded systems domain represents a class of systems that have high requirements on cost efficiency as well as run-time properties such as timeliness and dependability. The research on componentbased systems has produced component technologies for guaranteeing real-time properties. However, the issue of saving resources by allocating several components to real-time tasks has gained little focus. Trade-offs when allocating components to tasks are, e.g., CPU-overhead, footprint and integrity. In this paper we present a general approach for allocating components to real-time tasks, while utilizing existing real-time analysis to ensure a feasible allocation. We demonstrate that CPU-overhead and memory consumption can be reduced by as much as 48% and 32% respectively for industrially representative systems.
It has long been recognized that one of the fundamental difficulties in the estimation of two-view epipolar geometry is the capability of handling outliers. In this paper, we develop a fast and tractable algorithm that maximizes the number of inliers under the assumption of a purely translating camera. Compared to classical random sampling methods, our approach is guaranteed to compute the optimal solution of a cost function based on reprojection errors and it has better time complexity. The performance is in fact independent of the inlier/outlier ratio of the data.This opens up for a more reliable approach to robust ego-motion estimation. Our basic translation estimator can be embedded into a system that computes the full camera rotation. We demonstrate the applicability in several difficult settings with large amounts of outliers. It turns out to be particularly well-suited for small rotations and rotations around a known axis (which is the case for cellular phones where the gravitation axis can be measured). Experimental results show that compared to standard RANSAC methods based on minimal solvers, our algorithm produces more accurate estimates in the presence of large outlier ratios.
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