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Development Toward Autonomous Systems
Published in Ulrich Rembold, Robot Technology and Applications, 2020
To develop an autonomous mobile robot moving in a cluttered environment, it is necessary to use fast, three-dimensional, collision-free motion planning. Several methods have been proposed. Many of the approaches taken utilize the configuration space technique. This method is computationally very expensive. It requires, first, mapping a world description into a configuration space (i.e., generating the C-space obstacles). In general, this step consumes a great deal of time and memory. Furthermore, the search must be performed in a high dimensional space. Another approach is to use an octree representation in a three-dimensional Cartesian space. An octree is a recursive decomposition of a cubic space into subcubes. To find a collision-free path, special search techniques are applied to the octree.
Development of dynamic safety envelopes for autonomous remotely operated underwater vehicles
Published in Stein Haugen, Anne Barros, Coen van Gulijk, Trond Kongsvik, Jan Erik Vinnem, Safety and Reliability – Safe Societies in a Changing World, 2018
J. Hegde, E.H. Henriksen, I.B. Utne, I. Schjølberg
An Octree is used to generate the dynamic safety envelopes. Octree is a recursive tree data structure, which consists of spatial cubes named Octants. Each Octant can further be divided into eight child Octants. Figure 1 illustrates the Level 1 and the Level 2 Octree rendering with the AROV in the center of the Octree. In the Level 1 Octree, eight cubes surround the AROV and in the Level 2 Octree sixty four cubes surround the AROV. Each of the cubes are allocated an unique identifier and linked to a safe subsea traffic rule. The subsea traffic rule aims to maximize the horizontal and vertical seperation from the identified obstacle. If an obstacle is detected in one or more Octants, a suitable subsea traffic rule is suggested to the AROV or the human operator.
Manufacturing criteria in hybrid modular tools: How to combine additive and subtractive processes
Published in Paulo Jorge Bártolo, Artur Jorge Mateus, Fernando da Conceição Batista, Henrique Amorim Almeida, João Manuel Matias, Joel Correia Vasco, Jorge Brites Gaspar, Mário António Correia, Nuno Carpinteiro André, Nuno Fernandes Alves, Paulo Parente Novo, Pedro Gonçalves Martinho, Rui Adriano Carvalho, Virtual and Rapid Manufacturing, 2007
O. Kerbrat, P. Mognol, J.Y. Hascoet
An octree is a tree data structure, which represents a three-dimensional object by the division of space into small cubic boxes, or small parallelepipeds. The size of each box depends on the local geometric complexity of object represented (Kim, 1998). Each box in space corresponds to a node in the tree and each node is referred to as an octant. To construct an octree, the object is first enclosed by the smallest box (octant) that can completely contain the object in any direction. This octant (a cube or a parallelepiped) makes up the root level of the octree. It is then subdivided into 8 sub-octants which then represent the first level. The octants are classified into three categories: black (full), white (empty) and grey (partially filled). Black octants are those that are completely contained in the object of interest, whereas white ones are those that are completely outside the object. Grey octants are those that are partially inside and outside of the object. The subdivision process is performed until a desired resolution is reached. The specified accuracy is used to determine the final size of the smallest octants (Ding et al., 2004). The advantages of using an octree decomposition model are that the decomposition model can acquire relatively high accuracy and the fact that the special location of an octant is determined by an index code. With these codes the position of each octant could be easily found and the geometric information such as center point and edge lengths could thus be calculated.
Sparse-View Cone-Beam CT Reconstruction by Bar-by-Bar Neural FDK Algorithm
Published in Nondestructive Testing and Evaluation, 2023
Siqi Wang, Tatsuya Yatagawa, Yutaka Ohtake, Hiromasa Suzuki
To accelerate the training process, we introduce an adaptive data sampling based on the octree structure, where parts with more complicated structures are sampled more densely to obtain the training samples. An octree is a tree structure where each node, also called an ‘octant’, has eight child nodes obtained by dividing the parent node along x-, y- and z-axes. For a 3D CT image, it is used to compress the volume data [34], where a node is recursively divided into eight when the variation of CT values of voxels included by the node gets small enough. Thus, we can sample voxels more densely from the regions where the CT values vary spatially. Specifically, octree sampling performs the following steps by starting with the entire volume as the root node.
Information extraction system for urban planning and governance using LiDAR based 3D repository
Published in Journal of Spatial Science, 2023
Jayati Vijaywargiya, Anandakumar M. Ramiya
A key challenge in information extraction from point-cloud data is the unstructured and irregular nature of point clouds (Poux et al. 2017). Few of the popular approaches for structuring point data are based on spatial proximity. K-d trees are not the best choice for spatial subdivision when there is a possibility that point clouds can be updated, as the tree structure becomes unbalanced when point data are added or removed (Richter and Döllner 2014). Octree divides a 3D scene into multiple hierarchical cubes using uniform spatial subdivision (Richter and Döllner 2014). However, octree structuralism lacks the ability to dynamically adjust its tree structure based on actual object layout (Zhu et al. 2007).
A multi-scale VR navigation method for VR globes
Published in International Journal of Digital Earth, 2019
In a practical application, considering that virtual globe systems generally load scenes in a multi-threaded asynchronous mode, the octree structure is dynamically established in this study; i.e. the octree structure is updated each time a drawing object (mesh) is loaded into the scene. The octree is traversed, and each drawing object is inserted into the corresponding node of the octree. If the corresponding node is a leaf node and the total number of internal vertices exceeds the threshold value, the node is split. Refer to algorithm 1 for details.