Obstacle-Aware Indoor Pathfinding Using Point Clouds

Díaz-Vilariño, L.; Bogusławski, Paweł; Khoshelham, Kourosh; Lorenzo, Henrique

doi:10.3390/ijgi8050233

Cited by 17 publications

(11 citation statements)

References 41 publications

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“…Armeni [9] proposed a semantic parsing method that parsed the whole building into disjoint spaces and learnt candidate windows' geometric and appearance features, then considered the context among semantic elements. Díaz-Vilariño [8] et al classified the point cloud to obtain the geometry of floors, walls and ceilings by intersecting planes according to their adjacency relationships. The final elements were detected by an energy function using the off-the-shelf LP/MIP solver and Structured SVM [23].…”

Section: A Point Cloud Semantic Labellingmentioning

confidence: 99%

See 1 more Smart Citation

Automatic 3-D Reconstruction of Indoor Environment With Mobile Laser Scanning Point Clouds

Cui

Wang

et al. 2019

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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3D modelling of indoor environment plays an important role in various applications such as indoor navigation, BIM (Building Information Modelling), interactive visualization, emergency response, and so on. While automated reconstruction of 3D models from point clouds is receiving more and more attention. Indoor modelling remains a challenging task in terms of dealing with the complexity of indoor environment, the level of automation and restrictions of input data. To address these issues, an automatic indoor reconstruction method that quickly and effectively reconstructs indoor environment of multi-floors and multi-rooms using both point clouds and trajectories from mobile laser scanning (MLS) is proposed. The proposed automatic method of parametric structure modelling comprises of three steps. Firstly, structural elements, such as doors, windows, walls, floors, and ceilings, are extracted based on the geometric and semantic features of point clouds. Then, the point clouds are automatically segmented into disjoint segments likes rooms through a combination of visibility analysis and physical constraints of the structural elements, which ensures the integrity of the room-space partitions and yields priors for the definition of point cloud label for reconstructed model. Finally, 3D models of individual rooms are Manuscript Yang Cui received the B.S. and M.S. degrees in school of surveying and geographical sciences from Liaoning Engineering Technology University, China, in 2013 and 2016. She is currently reading doctorate of the college of information engineering, Shenzhen University. Her research interests include laser scanning and computer graphics, with a focus on 3-D indoor modeling.Qing quan Li received the B.S. degree in engineering survey, the M.S. degree, and the Ph.D. degree in photogrammetry and remote sensing from Wuhan

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Section: A Point Cloud Semantic Labellingmentioning

confidence: 99%

“…A number of approaches have been proposed for indoor modelling from point clouds. Some methods classify indoor point clouds and assign them with semantic labels, such as floors, walls, ceilings, and other objects [8], [9]. However, the resulting models have only semantic information but no structure attributes.…”

Section: Introductionmentioning

confidence: 99%

Automatic 3-D Reconstruction of Indoor Environment With Mobile Laser Scanning Point Clouds

Cui

Wang

et al. 2019

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…A second group of papers focuses on indoor navigation. The first of them [6] is concerned with the detection of obstacles in indoor navigation. It proposes a complete processing chain, from the LiDAR point cloud to the computation of the final path, through the modelling of the indoor environment.…”

Section: Content Of the Issuementioning

confidence: 99%

Multidimensional and Multiscale GIS

Guilbert

Bogusławski

Işıkdağ

2019

IJGI

Self Cite

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The advent of new data collection technologies, such as LiDAR and drones, have made geospatial data available in large amounts and at low costs. While access to data is getting easier, geospatial tools have to evolve towards further automation and guarantee the reproducibility of the process and the quality of the results. As such, algorithms and data structures for handling geospatial data also need to be more and more robust and efficient to model complex, multidimensional geospatial phenomena in GISystems and provide higher levels of analysis. Articles in this special issue address two complementary aspects of the problem. They introduce new algorithms and data structures that allow for a more efficient handling of multidimensional data but also present complete processing chains dealing with the integration and the dissemination of multidimensional data.

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“…Indoor navigation systems are rapidly growing with amazing technologies. Typically, these systems are used for assistance of disabled or aged people, robot path planning, AR gaming, tourist's guidance, and training [1,2]. Indoor navigation systems are aimed with either infrastructure-dependent systems [3,4] which use sensors embedded in the environment for user tracking or infrastructure-independent systems [5].…”

Section: Introductionmentioning

confidence: 99%

“…A recent study [6] identified the following challenges usually considered for navigation and localization of user in large scale environments. (1) Accuracy and continuity: The accuracy and continuity of locations are important, especially for visually impaired people. For a real-time guidance, a localization accuracy of about two meters is desired.…”

Section: Introductionmentioning

confidence: 99%

A Generic Approach toward Indoor Navigation and Pathfinding with Robust Marker Tracking

2019

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Indoor navigation and localization has gained a key attention of the researchers in the recent decades. Various technologies such as WiFi, Bluetooth, Ultra Wideband (UWB), and Radio-frequency identification (RFID) have been used for indoor navigation and localization. However, most of these existing methods often fail in providing a reasonable solution to the key challenges such as implementation cost, accuracy and extendibility. In this paper, we proposed a low-cost, and extendable framework for indoor navigation. We used simple markers printed on the paper, and placed on ceilings of the building. These markers are detected by a smartphone’s camera, and the audio and visual information associated with these markers are used as a user guidance. The system finds shortest path between any two arbitrary nodes for user navigation. In addition, it is extendable having the capability to cover new sections by installing new nodes at any place in the building. The system can be used for guidance of the blind people, tourists and new visitors in an indoor environment. The evaluation results reveal that the proposed system can guide users toward their destination in an efficient and accurate manner.

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Obstacle-Aware Indoor Pathfinding Using Point Clouds

Cited by 17 publications

References 41 publications

Automatic 3-D Reconstruction of Indoor Environment With Mobile Laser Scanning Point Clouds

Automatic 3-D Reconstruction of Indoor Environment With Mobile Laser Scanning Point Clouds

Multidimensional and Multiscale GIS

A Generic Approach toward Indoor Navigation and Pathfinding with Robust Marker Tracking

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