Automatic floor plan analysis and recognition

Pizarro, Pablo N.; Hitschfeld, Nancy; Sipiran, Ivan; Saavedra, José M.

doi:10.1016/j.autcon.2022.104348

Cited by 33 publications

(19 citation statements)

References 72 publications

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“…motion trajectories of single agents as well as of individual agents in a single or in multiple squads. As input, a building plan is required, which is usually available for newer buildings and may be interpreted by other means (compare [15] for an overview). The map is segmented into individual rooms and their connecting doorways.…”

Section: Graph-based Tactics-informed Optimal Planningmentioning

confidence: 99%

Finding Moving Operators in Firefighting Operations Based on Multi-Goal Next-Best-View Exploration

Mandischer

Gurtler

Dobler

et al. 2022

2022 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR)

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Firefighting is a complex, yet low automated task. To mitigate ergonomic and safety related risks on the human operators, robots could be deployed in a collaborative approach. To allow human-robot teams in firefighting, important basics are missing. Amongst other aspects, the robot must predict the human motion as occlusion is ever-present. In this work, we propose a novel motion prediction pipeline for firefighters' squads in indoor search and rescue. The squad paths are generated with an optimal graph-based planning approach representing firefighters' tactics. Paths are generated per room which allows to dynamically adapt the path locally without global re-planning. The motion of singular agents is simulated using a modification of the headed social force model. We evaluate the pipeline for feasibility with a novel data set generated from real footage and show the computational efficiency.

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Section: Graph-based Tactics-informed Optimal Planningmentioning

confidence: 99%

Finding Moving Operators in Firefighting Operations Based on Multi-Goal Next-Best-View Exploration

Mandischer

Gurtler

Dobler

et al. 2022

2022 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR)

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“…The International Energy Agency (IEA) estimates that by 2050, the global building floor space will increase by about 235 billion m 2 to accommodate the growing population and rising living standards [1]. Indoor spatial information plays a crucial role in various applications, such as indoor location services for indoor navigation, path planning, emergency evacuation, indoor scene modeling, building plan retrieval, and augmented reality [2]. The current data sources used to extract indoor spatial information mainly include indoor laser point cloud, building information modeling (BIM), and architectural design plan.…”

Section: Introductionmentioning

confidence: 99%

Multitask floor plan analysis based on multi-scale and self-attention

Han,

Qiao,

Guo

et al. 2023

International Conference on Remote Sensing, Surveying, and Mapping (RSSM 2023)

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With the continuous acceleration of urbanization and the rapid development of modern architectural technology, there are increasingly large public buildings and more complex indoor space patterns. At present, there are two main challenges in the analysis of interior floor plans: the first is that the objects in the image show scale diversity; the second is that due to the limitations of the convolution layer itself, ordinary convolutional neural networks have some difficulties in capturing global semantic information. In this paper, we propose a multi-task convolutional neural network model based on the multi-scale and polarized self-attention mechanism to improve the multi-scale information aggregation and global semantic information capture capabilities of semantic segmentation networks. Experiments on the public data set CubiCasa5K show that the proposed model can more accurately complete the identification of building components and the extraction of spatial area information in the floor plan.

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“…Therefore, intelligent and automated design is needed to improve design efficiency and give valuable design references for engineers. [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28] The preliminary but critical seismic isolation scheme design process can significantly affect subsequent optimal designs. In scheme design, precise structural analysis is not as important as the determination of isolation bearing close to the ideal design.…”

Section: Introductionmentioning

confidence: 99%

“…With the rise of deep learning, intelligent structural design methods that can learn from existing design data are rapidly evolving, thereby opening up new possibilities. [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28] Liao et al, 14,15 Lu et al, 16 Fei et al, 17 , 18 Zhao et al, 19,20 Pizarro et al, 24 and Fu et al 25 undertook comprehensive research to develop a generative adversarial network (GAN)-based intelligent design approaches for shear walls, beams, and frame-core tube structures; Chang et al 13 and Zhao et al 21 developed graph neural network (GNN)-based structural design methods; Hayashi et al, 26 Zhu et al, 27 and Jeong et al 28 used reinforcement learning for structural design. Among those methods, GAN is one of the most effective and extensively utilized technologies for generating structural designs, due to its powerful generation ability.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Base‐isolation design of shear wall structures using physics‐rule‐co‐guided self‐supervised generative adversarial networks

Liao

Wang

Fei

et al. 2023

Earthq Engng Struct Dyn

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Seismic isolation can significantly improve the seismic resilience of buildings, resulting in a growing demand for seismic isolation designs. Meanwhile, the deep generative network‐based intelligent design can significantly increase scheme design efficiency. However, the performance of existing intelligent scheme designs is constrained by data quality and quantity. The limited availability of isolation design data hinders the development of intelligent seismic isolation design. Therefore, there is an emerging demand to establish an intelligent scheme design method that is free from data constraints and that can learn the physical mechanism and design rules. Consequently, this study proposes a physics‐rule‐co‐guided self‐supervised generative adversarial network (GAN) that can generate the layout and parameters of seismic isolation bearings by inputting the layout drawings of the shear wall structures. The critical physics‐rule‐co‐guided network model consists of a physics estimator, rule evaluator, discriminator, and design generator. The physics estimator is a deep neural network‐based surrogate model for predicting the mechanical response of an isolated structure, whereas the rule evaluator is a tensor operation‐based loss calculator that considers design rules. Furthermore, the proposed GAN model masters the schematic design ability of the seismic isolation of shear wall structures through multiphase hybrid learning of the pseudo‐labels, physical mechanism, and isolation design rules, obviating the need for ground‐truth data. Case studies also prove the rationality of the method, where the design results can effectively meet the code requirements and reduce the seismic response of the structure.

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Automatic floor plan analysis and recognition

Cited by 33 publications

References 72 publications

Finding Moving Operators in Firefighting Operations Based on Multi-Goal Next-Best-View Exploration

Finding Moving Operators in Firefighting Operations Based on Multi-Goal Next-Best-View Exploration

Multitask floor plan analysis based on multi-scale and self-attention

Base‐isolation design of shear wall structures using physics‐rule‐co‐guided self‐supervised generative adversarial networks

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