Automatic Extraction of a Navigation Graph Intended for Indoorgml From an Indoor Point Cloud

Flikweert, Puck; Peters, Ravi; Díaz-Vilariño, L.; Voûte, R.; Staats, Bart

doi:10.5194/isprs-annals-iv-2-w5-271-2019

Cited by 15 publications

(10 citation statements)

References 26 publications

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“…Some of the approaches are limited to specific building structures such as Manhattan World or 2.5D buildings [Ikehata et al, 2015, Turner et al, 2015, others offer more flexibility in terms of adoption of multi-story buildings and complex structures [Ochmann et al, 2019,Bassier and Vergauwen, 2020,Tran and Khoshelham, 2020. For our simulation purposes, we are focusing on reconstructing the volumetric walls by exploiting the topology between the permanent structures similar to [Nikoohemat et al, 2020b,Ochmann et al, 2016, detecting doors [Nikoohemat et al, 2018, Flikweert et al, 2019 and modeling multi-story buildings [Macher et al, 2017, Ochmann et al, 2019.…”

Section: Related Workmentioning

confidence: 99%

Point Cloud Based 3d Models for Agent Based Simulations in Social Distancing and Evacuation

Nikoohemat

Godoy²,

Valkhoff³

et al. 2021

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Point clouds serve as the raw material for various models, such as Building Information Models (BIM). In this work, we investigate the reconstruction steps needed to create models that can be utilized directly for agent-based simulations. The input data for the reconstruction is captured with an indoor mobile mapping system. To show the prominence of this idea, we run social distancing and evacuation simulations on the reconstructed models. The simulations are run with multiple agents using a vision-based pedestrian model and A*-based path finding algorithm. The limitations of this approach are discussed. The video of the simulation is shared with the audience.Link to the video: https://youtu.be/r2D3IxXt7Ls

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Section: Related Workmentioning

confidence: 99%

Point Cloud Based 3d Models for Agent Based Simulations in Social Distancing and Evacuation

Nikoohemat

Godoy²,

Valkhoff³

et al. 2021

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

Self Cite

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“…Sometimes, even additional postprocessing is needed to generate a reliable 3D representation of the environment (Luhmann et al, 2013). This delay of processing is not a problem for many SLAM use cases, as processes such as 'scan to BIM' (Wang, Cho and Kim, 2015) or generation of high quality navigation graphs (Staats et al, 2017;Flikweert et al, 2019;Nikoohema et al, 2020) do not require instant accessibility. First responders however, need the resulting data as soon as possible due to the dynamic environment of first responder operations (Kapucu and Garayev, 2011;Seppänen and Virrantaus, 2015).…”

Section: Slam Research Gap For First Respondersmentioning

confidence: 99%

Creating 3d Indoor First Responder Situation Awareness in Real-Time Through a Head-Mounted Ar Device

Bram

Voûte

Verbree

2021

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Emergency operations are a key example for the need of digital twins in the way it is complex, urgent and uncertain. First, the process is complex, as many organizations are involved. Second, it is urgent, as most damage is done in the first moments of an emergency. Third, it is uncertain, as situational conditions tend to change quickly.For outdoor operations, spatial information systems help in creating an overview of the situation, for example by displaying positions of first responder units involved with the incident. However, spatial data of indoor environments is scarce. Static information of the building, such as floor plans, are often outdated or non-existent. Dynamic operational data such as positions of first responders within the building are only available in a very limited way as well, and often without visual representation.To create situation awareness of indoor first responder operation environments, this paper successfully proposes a proof of concept with two objectives. First, the proof of concept will collect spatial environment data in the form of mapping and tracking data by using a Microsoft HoloLens. This means the geometry of the building will be collected, together with traversed routes within the building. Second, the data will be streamed and displayed to a remote first responder coordinator in real-time to create a common operational picture. This enables the coordinator to quickly build situation awareness of the operation environment, enabling the coordinator to improve the quality of decisions, thereby improving first responder performance. The proof of concept showed that situation awareness on all three levels increases with the real-time (live) availability (visualisations) of 3D indoor environments. This concept needs to be tested further on usability and performance.

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“…Indoor Point Cloud Application. Indoor scene semantics based on point cloud is essential for many applications, such as planning, localization and navigation services (Flikweert et al, 2019, Quintana et al, 2016. However, indoor environments pose specific challenges for point cloud semantic segmentation due to complex layout, variety of object types and occlusions (Ochmann et al, 2016, Pang et al, 2018.…”

Section: Related Workmentioning

confidence: 99%

A Comparative Study of Point Clouds Semantic Segmentation Using Three Different Neural Networks on the Railway Station Dataset

Lumban-Gaol

Chen

Smit

et al. 2021

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Point cloud data have rich semantic representations and can benefit various applications towards a digital twin. However, they are unordered and anisotropically distributed, thus being unsuitable for a typical Convolutional Neural Networks (CNN) to handle. With the advance of deep learning, several neural networks claim to have solved the point cloud semantic segmentation problem. This paper evaluates three different neural networks for semantic segmentation of point clouds, namely PointNet++, PointCNN and DGCNN. A public indoor scene of the Amersfoort railway station is used as the study area. Unlike the typical indoor scenes and even more from the ubiquitous outdoor ones in currently available datasets, the station consists of objects such as the entrance gates, ticket machines, couches, and garbage cans. For the experiment, we use subsets from the data, remove the noise, evaluate the performance of the selected neural networks. The results indicate an overall accuracy of more than 90% for all the networks but vary in terms of mean class accuracy and mean Intersection over Union (IoU). The misclassification mainly occurs in the classes of couch and garbage can. Several factors that may contribute to the errors are analyzed, such as the quality of the data and the proportion of the number of points per class. The adaptability of the networks is also heavily dependent on the training location: the overall characteristics of the train station make a trained network for one location less suitable for another.

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Automatic Extraction of a Navigation Graph Intended for Indoorgml From an Indoor Point Cloud

Cited by 15 publications

References 26 publications

Point Cloud Based 3d Models for Agent Based Simulations in Social Distancing and Evacuation

Point Cloud Based 3d Models for Agent Based Simulations in Social Distancing and Evacuation

Creating 3d Indoor First Responder Situation Awareness in Real-Time Through a Head-Mounted Ar Device

A Comparative Study of Point Clouds Semantic Segmentation Using Three Different Neural Networks on the Railway Station Dataset

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