Navigation in Indoor Voxel Models

Gorte, B.G.H.; Zlatanova, Sisi; Fadli, Fodil

doi:10.5194/isprs-annals-iv-2-w5-279-2019

Cited by 21 publications

(14 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Once a distance transform is signed, we can determine if a point is within or out of an object [49]. The use of distance transform is versatile including computer graphics, digital image processing (e.g., blurring effects, skeletonizing), path planning and pathfinding [20].…”

Section: Binary and Non-binary Voxelisationmentioning

confidence: 99%

“…Voxel-based methods are extensively applied for the processing of point clouds [15][16][17]. A quick voxelisation is suggested to derive navigable areas for pedestrian simulation [18][19][20] and collision detection [21,22]. As we can see voxels are highly applicable in various domains due to their discrete representation creating a continuous phenomenon in space.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Voxelisation Algorithms and Data Structures: A Review

Aleksandrov

Zlatanova

Heslop

2021

Sensors

View full text Add to dashboard Cite

Voxel-based data structures, algorithms, frameworks, and interfaces have been used in computer graphics and many other applications for decades. There is a general necessity to seek adequate digital representations, such as voxels, that would secure unified data structures, multi-resolution options, robust validation procedures and flexible algorithms for different 3D tasks. In this review, we evaluate the most common properties and algorithms for voxelisation of 2D and 3D objects. Thus, many voxelisation algorithms and their characteristics are presented targeting points, lines, triangles, surfaces and solids as geometric primitives. For lines, we identify three groups of algorithms, where the first two achieve different voxelisation connectivity, while the third one presents voxelisation of curves. We can say that surface voxelisation is a more desired voxelisation type compared to solid voxelisation, as it can be achieved faster and requires less memory if voxels are stored in a sparse way. At the same time, we evaluate in the paper the available voxel data structures. We split all data structures into static and dynamic grids considering the frequency to update a data structure. Static grids are dominated by SVO-based data structures focusing on memory footprint reduction and attributes preservation, where SVDAG and SSVDAG are the most advanced methods. The state-of-the-art dynamic voxel data structure is NanoVDB which is superior to the rest in terms of speed as well as support for out-of-core processing and data management, which is the key to handling large dynamically changing scenes. Overall, we can say that this is the first review evaluating the available voxelisation algorithms for different geometric primitives as well as voxel data structures.

show abstract

Section: Binary and Non-binary Voxelisationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Voxelisation Algorithms and Data Structures: A Review

Aleksandrov

Zlatanova

Heslop

2021

Sensors

View full text Add to dashboard Cite

show abstract

“…While appropriate for realistic visualisation, Boundary representation can fall short in performing spatial operations as volume validation and computation. Therefore, for many applications Spatial Occupancy Enumeration (e.g., voxels [125,126]) can be seen as alternative [127,128]. There is no strict pattern which geometric representation is employed in different disciplines.…”

Section: Spaces Representationmentioning

confidence: 99%

Spaces in Spatial Science and Urban Applications—State of the Art Review

Zlatanova¹,

Yan²,

Wang

et al. 2020

IJGI

View full text Add to dashboard Cite

In spatial science and urban applications, “space" is presented by multiple disciplines as a notion referencing our living environment. Space is used as a general term to help understand particular characteristics of the environment. However, the definition and perception of space varies and these variations have to be harmonised. For example, space may have diverse definitions and classification, the same environment may be abstracted/modelled by contradicting notions of space, which can lead to inconsistencies and misunderstandings. In this paper, we seek to investigate and document the state-of-the-art in the research of “space” regarding its definition, classification, modelling and utilization (2D/3D) in spatial sciences and urban applications. We focus on positioning, navigation, building micro-climate and thermal comfort, landscape, urban planning and design, urban heat island, interior design and planning, transportation and intelligent space. We review 147 research papers, technical reports and on-line resources. We compare the presented space concepts with respect to five criteria—classification, boundary, modelling components, use of standards and granularity. The review inventory is intended for both scientists and professionals in the spatial industry, such as companies, national mapping agencies and governments, and aim to provide a reference to better understand and employ the “space” while working across disciplines.

show abstract

“…Moreover, such an alignment process-also known as pose normalization [49]-can still be a reasonable choice, even if the respective indoor reconstruction method does not presuppose a Manhattan World-compliant building structure. This is for instance the case when a respective indoor reconstruction approach makes use of a voxel grid or octree representation of the input data [50][51][52][53]. Even if a voxel-based indoor reconstruction approach is able to handle building structures deviating from the Manhattan World assumption, having room surfaces aligned with the coordinate axes and thus with the voxel grid will result in a cleaner and visually more appealing reconstruction in the voxel space.…”

Section: Introductionmentioning

confidence: 99%

Pose Normalization of Indoor Mapping Datasets Partially Compliant with the Manhattan World Assumption

et al. 2021

View full text Add to dashboard Cite

Due to their great potential for a variety of applications, digital building models are well established in all phases of building projects. Older stock buildings however frequently lack digital representations, and creating these manually is a tedious and time-consuming endeavor. For this reason, the automated reconstruction of building models from indoor mapping data has arisen as an active field of research. In this context, many approaches rely on simplifying suppositions about the structure of buildings to be reconstructed such as, e.g., the well-known Manhattan World assumption. This however not only presupposes that a given building structure itself is compliant with this assumption, but also that the respective indoor mapping dataset is aligned with the coordinate axes. Indoor mapping systems, on the other hand, typically initialize the coordinate system arbitrarily by the sensor pose at the beginning of the mapping process. Thus, indoor mapping data need to be transformed from the local coordinate system, resulting from the mapping process, to a local coordinate system where the coordinate axes are aligned with the Manhattan World structure of the building. This necessary preprocessing step for many indoor reconstruction approaches is also frequently known as pose normalization. In this paper, we present a novel pose-normalization method for indoor mapping point clouds and triangle meshes that is robust against large portions 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 was 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 was conducted. Subsequently, a rotation around the resulting vertical axis was determined that aligned the dataset horizontally with the axes of the local coordinate system. The performance of the proposed method was evaluated quantitatively on several publicly available indoor mapping datasets of different complexity. The achieved results clearly revealed that our method is able to consistently produce correct poses for the considered datasets for different input rotations with high accuracy. The implementation of our method along with the code for reproducing the evaluation is made available to the public.

show abstract

Navigation in Indoor Voxel Models

Cited by 21 publications

References 7 publications

Voxelisation Algorithms and Data Structures: A Review

Voxelisation Algorithms and Data Structures: A Review

Spaces in Spatial Science and Urban Applications—State of the Art Review

Pose Normalization of Indoor Mapping Datasets Partially Compliant with the Manhattan World Assumption

Contact Info

Product

Resources

About