Centre dedocumentation
scientifique

Automatic extraction of indoor spatial information from floor plan image: A patch-based deep learning methodology application on large-scale complex buildings / Hyunjung Kim in ISPRS International journal of geo-information, vol 10 n° 12 (December 2021)  

Public [article]  inISPRS International journal of geo-information > vol 10 n° 12 (December 2021) . - n° 828 Titre : Automatic extraction of indoor spatial information from floor plan image: A patch-based deep learning methodology application on large-scale complex buildings Type de document : Article/Communication Auteurs : Hyunjung Kim, Auteur ; Seongyong Kim, Auteur ; Kiyun Yu, Auteur Année de publication : 2021 Article en page(s) : n° 828 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] apprentissage profond [Termes IGN] bâtiment [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] détection automatique [Termes IGN] indoorGML [Termes IGN] positionnement en intérieur [Termes IGN] reconstruction 3D du bâti Résumé : (auteur) Automatic floor plan analysis has gained increased attention in recent research. However, numerous studies related to this area are mainly experiments conducted with a simplified floor plan dataset with low resolution and a small housing scale due to the suitability for a data-driven model. For practical use, it is necessary to focus more on large-scale complex buildings to utilize indoor structures, such as reconstructing multi-use buildings for indoor navigation. This study aimed to build a framework using CNN (Convolution Neural Networks) for analyzing a floor plan with various scales of complex buildings. By dividing a floor plan into a set of normalized patches, the framework enables the proposed CNN model to process varied scale or high-resolution inputs, which is a barrier for existing methods. The model detected building objects per patch and assembled them into one result by multiplying the corresponding translation matrix. Finally, the detected building objects were vectorized, considering their compatibility in 3D modeling. As a result, our framework exhibited similar performance in detection rate (87.77%) and recognition accuracy (85.53%) to that of existing studies, despite the complexity of the data used. Through our study, the practical aspects of automatic floor plan analysis can be expanded. Numéro de notice : A2021-926 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi10120828 Date de publication en ligne : 10/12/2021 En ligne : https://doi.org/10.3390/ijgi10120828 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99289 [article]

Variational bayesian compressive multipolarization indoor radar imaging / Van Ha Tang in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 9 (September 2021)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > Vol 59 n° 9 (September 2021) . - pp 7459 - 7474 Titre : Variational bayesian compressive multipolarization indoor radar imaging Type de document : Article/Communication Auteurs : Van Ha Tang, Auteur ; Abdesselam Bouzerdoum, Auteur ; Son Lam Phung, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 7459 - 7474 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] acquisition comprimée [Termes IGN] détection à travers-le-mur [Termes IGN] estimation bayesienne [Termes IGN] fouillis d'échos [Termes IGN] image radar [Termes IGN] inférence statistique [Termes IGN] modèle stochastique [Termes IGN] polarisation [Termes IGN] positionnement en intérieur [Termes IGN] reconstruction d'image Résumé : (auteur) This article introduces a probabilistic Bayesian model for addressing the problem of compressive multipolarization through-wall radar imaging (TWRI). The proposed approach formulates the task of wall-clutter mitigation and multipolarization image reconstruction as a Bayesian inference problem for a joint distribution between observed radar measurements and latent wall-clutter matrix and indoor target images. The joint probability distribution incorporates three prior beliefs: low-dimensional structure of the wall reflections, group sparsity structure of the target images, and joint sparsity among the polarization images. These signal attributes are modeled through hierarchical priors, whose parameters and hyperparameters are treated with a full Bayesian formulation. Furthermore, this article presents a variational Bayesian inference algorithm that estimates wall-clutter and multipolarization images as posterior distributions and optimizes the model parameters and hyperparameters simultaneously. Experimental results on simulated and real radar data show that the proposed model is very effective at removing wall clutter and enhancing target localization even when the radar measurements are significantly reduced. Numéro de notice : A2021-647 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2021.3051955 Date de publication en ligne : 26/01/2021 En ligne : https://doi.org/10.1109/TGRS.2021.3051955 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98354 [article]

Spatial knowledge acquisition with virtual semantic landmarks in mixed reality-based indoor navigation / Bing Liu in Cartography and Geographic Information Science, vol 48 n° 4 (July 2021)  

Public [article]  inCartography and Geographic Information Science > vol 48 n° 4 (July 2021) . - pp 305 - 319 Titre : Spatial knowledge acquisition with virtual semantic landmarks in mixed reality-based indoor navigation Type de document : Article/Communication Auteurs : Bing Liu, Auteur ; Linfang Ding, Auteur ; Liqiu Meng, Auteur Année de publication : 2021 Article en page(s) : pp 305 - 319 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] conception orientée utilisateur [Termes IGN] GPS assisté pour la navigation (technologies) [Termes IGN] hologramme [Termes IGN] information sémantique [Termes IGN] navigation virtuelle [Termes IGN] point de repère [Termes IGN] positionnement en intérieur [Termes IGN] questionnaire [Termes IGN] réalité mixte [Termes IGN] téléphone intelligent [Termes IGN] utilisateur civil Résumé : (auteur) Landmarks are essential and widely used in human navigation. However, many indoor environments lack visually salient landmarks, which leads to difficulties in navigating in and learning complex and similar-looking indoor environments. In this study, we designed and implemented virtual semantic landmarks in Mixed Reality (MR)-based indoor environments and conducted a user study to explore whether such landmarks can assist spatial knowledge acquisition during navigation. More specifically, we employed the untethered, head-mounted mixed reality device Microsoft HoloLens and used iconic holograms to show the semantic landmarks. In the user study, we used sketch map, landmark locating tasks and interview to assess the results of the spatial knowledge acquisition and collect advice on improving the MR-based navigation interface. The results show that virtual semantic landmarks can assist the acquisition of corresponding knowledge, as such landmarks were labeled second most often in landmark locating task. In addition, individual cases show that head-mounted mixed reality devices may influence not only vision, but also height or time perception of certain users. Our result can be applied to facilitate the design of MR-based navigation interfaces and assist spatial knowledge acquisition. Numéro de notice : A2021-445 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/15230406.2021.1908171 Date de publication en ligne : 22/04/2021 En ligne : https://doi.org/10.1080/15230406.2021.1908171 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97852 [article]

Réservation

Réserver ce document

Exemplaires (1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
032-2021041	SL	Revue	Centre de documentation	Revues en salle	Disponible

Indoor mapping and modeling by parsing floor plan images / Yijie Wu in International journal of geographical information science IJGIS, vol 35 n° 6 (June 2021)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 35 n° 6 (June 2021) . - pp 1205 - 1231 Titre : Indoor mapping and modeling by parsing floor plan images Type de document : Article/Communication Auteurs : Yijie Wu, Auteur ; Jianga Shang, Auteur ; Pan Chen, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 1205 - 1231 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] carte d'intérieur [Termes IGN] chevauchement [Termes IGN] CityGML [Termes IGN] construction [Termes IGN] format Industry foudation classes IFC [Termes IGN] intégrité topologique [Termes IGN] mur [Termes IGN] optimisation spatiale [Termes IGN] positionnement en intérieur [Termes IGN] vectorisation Résumé : (auteur) A large proportion of indoor spatial data is generated by parsing floor plans. However, a mature and automatic solution for generating high-quality building elements (e.g., walls and doors) and space partitions (e.g., rooms) is still lacking. In this study, we present a two-stage approach to indoor mapping and modeling (IMM) from floor plan images. The first stage vectorizes the building elements on the floor plan images and the second stage repairs the topological inconsistencies between the building elements, separates indoor spaces, and generates indoor maps and models. To reduce the shape complexity of indoor boundary elements, i.e., walls and openings, we harness the regularity of the boundary elements and extract them as rectangles in the first stage. Furthermore, to resolve the overlaps and gaps of the vectorized results, we propose an optimization model that adjusts the rectangle vertex coordinates to conform to the topological constraints. Experiments demonstrate that our approach achieves a considerable improvement in room detection without conforming to Manhattan World Assumption. Our approach also outputs instance-separate walls with consistent topology, which enables direct modeling into Industry Foundation Classes (IFC) or City Geography Markup Language (CityGML). Numéro de notice : A2021-385 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1781130 Date de publication en ligne : 08/07/2020 En ligne : https://doi.org/10.1080/13658816.2020.1781130 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97642 [article]

Research on feature extraction method of indoor visual positioning image based on area division of foreground and background / Ping Zheng in ISPRS International journal of geo-information, vol 10 n° 6 (June 2021)  

Public [article]  inISPRS International journal of geo-information > vol 10 n° 6 (June 2021) . - n° 402 Titre : Research on feature extraction method of indoor visual positioning image based on area division of foreground and background Type de document : Article/Communication Auteurs : Ping Zheng, Auteur ; Danyang Qin, Auteur ; Bing Han, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 402 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] corrélation à l'aide de traits caractéristiques [Termes IGN] extraction de traits caractéristiques [Termes IGN] logiciel libre [Termes IGN] positionnement en intérieur [Termes IGN] Ransac (algorithme) [Termes IGN] SIFT (algorithme) [Termes IGN] SURF (algorithme) Résumé : (auteur) In the process of indoor visual positioning and navigation, difficult points often exist in corridors, stairwells, and other scenes that contain large areas of white walls, strong consistent background, and sparse feature points. Aiming at the problem of positioning and navigation in the real physical world where the walls with sparse feature points are difficult to be filled with pictures, this paper designs a feature extraction method, ARAC (Adaptive Region Adjustment based on Consistency) using Free and Open-Source Software and tools. It divides the image into foreground and background and extracts their features respectively, to achieve not only retain positioning information but also focus more energy on the foreground area which is favourable for navigation. In the test phase, under the combined conditions of illumination, scale and affine changes, the feature matching maps by the feature extraction algorithm proposed in this paper are compared with those by SIFT and SURF. Experiments show that the number of correctly matched feature pairs obtained by ARAC is better than SIFT and SURF, and whose time of feature extraction and matching is comparable to SURF, which verifies the accuracy and efficiency of the ARAC feature extraction method. Numéro de notice : A2021-518 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/ijgi10060402 Date de publication en ligne : 11/06/2021 En ligne : https://doi.org/10.3390/ijgi10060402 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97940 [article]

Visual positioning in indoor environments using RGB-D images and improved vector of local aggregated descriptors / Longyu Zhang in ISPRS International journal of geo-information, vol 10 n° 4 (April 2021)  

Permalink

An anchor-based graph method for detecting and classifying indoor objects from cluttered 3D point clouds / Fei Su in ISPRS Journal of photogrammetry and remote sensing, vol 172 (February 2021)  

Permalink

Adaptive filtering / Wenping Cao (2021)  

Permalink

[Bornage, lever topographique et lever d'intérieur] / Vincent Legrand (2021)

Permalink

GNSS/5G Hybridization for Urban Navigation / Anne-Marie Tobie (2021)  

Permalink

Intelligent sensors for positioning, tracking, monitoring, navigation and smart sensing in smart cities / Li Tiancheng (2021)  

Permalink

L’Ultra Wideband, un système de positionnement topographique sans satellite / Joël Van Cranenbroeck in XYZ, n° 165 (décembre 2020)

Permalink

Indoor positioning using PnP problem on mobile phone images / Hana Kubickova in ISPRS International journal of geo-information, vol 9 n° 6 (June 2020)  

Permalink

A proposal for modeling indoor–outdoor spaces through indoorGML, open location code and OpenStreetMap / Ruben Cantarero Navarro in ISPRS International journal of geo-information, vol 9 n° 3 (March 2020)  

Permalink

Improved indoor positioning based on range-free RSSI fingerprint method / Marcin Uradzinski in Journal of geodetic science, vol 10 n° 1 (January 2020)  

Permalink