654 research outputs found
Pose Normalization of Indoor Mapping Datasets Partially Compliant with the Manhattan World Assumption
In this paper, we present a novel pose normalization method for indoor
mapping point clouds and triangle meshes that is robust against large fractions
of the indoor mapping geometries deviating from an ideal Manhattan World
structure. In the case of building structures that contain multiple Manhattan
World systems, the dominant Manhattan World structure supported by the largest
fraction of geometries is determined and used for alignment. In a first step, a
vertical alignment orienting a chosen axis to be orthogonal to horizontal floor
and ceiling surfaces is conducted. Subsequently, a rotation around the
resulting vertical axis is determined that aligns the dataset horizontally with
the coordinate axes. The proposed method is evaluated quantitatively against
several publicly available indoor mapping datasets. Our implementation of the
proposed procedure along with code for reproducing the evaluation will be made
available to the public upon acceptance for publication
Performance Evaluation of Pathfinding Algorithms
Pathfinding is the search for an optimal path from a start location to a goal location in a given environment. In Artificial Intelligence pathfinding algorithms are typically designed as a kind of graph search. These algorithms are applicable in a wide variety of applications such as computer games, robotics, networks, and navigation systems. The performance of these algorithms is affected by several factors such as the problem size, path length, the number and distribution of obstacles, data structures and heuristics. When new pathfinding algorithms are proposed in the literature, their performance is often investigated empirically (if at all). Proper experimental design and analysis is crucial to provide an informative and non- misleading evaluation. In this research, we survey many papers and classify them according to their methodology, experimental design, and analytical techniques. We identify some weaknesses in these areas that are all too frequently found in reported approaches. We first found the pitfalls in pathfinding research and then provide solutions by creating example problems. Our research shows that spurious effects, control conditions provide solutions to avoid these pitfalls
Automatic Domain Decomposition in Finite Element Method – A Comparative Study
In this paper, an automatic data clustering approach is presented using some concepts of the graph theory. Some Cluster Validity Index (CVI) is mentioned, and DB Index is defined as the objective function of meta-heuristic algorithms. Six Finite Element meshes are decomposed containing two- and three- dimensional types that comprise simple and complex meshes. Six meta-heuristic algorithms are utilized to determine the optimal number of clusters and minimize the decomposition problem. Finally, corresponding statistical results are compared
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