One Way to Fill All the Concave Region in Grid-Based Map

Zhang, Ziying; Xu, Yang; Xu, Dong; Geng, Kang; Yang, Meng; Feng, Guangsheng

doi:10.1017/s0263574720000818

Cited by 2 publications

(1 citation statement)

References 25 publications

(29 reference statements)

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“…However, dealing with a huge number of cells may exponentially increase traversal time, therefore it will bring performance and scalability problems [17]. Quadtrees [18], PR quadtrees [19], skip quadtrees [20] and concave areas elimination [21] are the current solutions to solve the performance problems.…”

Section: Grid-based Mapmentioning

confidence: 99%

Extracting 3D Indoor Maps with Any Shape Accurately Using Building Information Modeling Data

Qiu

Wang

Qing-sheng

et al. 2021

IJGI

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Indoor maps lay the foundation for most indoor location-based services (LBS). Building Information Modeling (BIM) data contains multiple dimensional computer-aided design information. Some studies have utilized BIM data to automatically extract 3D indoor maps. A complete 3D indoor map consists of both floor-level maps and cross-floor paths. Currently, the floor-level indoor maps are mainly either grid-based maps or topological maps, and the cross-floor path generation schemes are not adaptive to building elements with irregular 3D shapes. To address these issues, this study proposes a novel scheme to extract an accurate 3D indoor map with any shape using BIM data. Firstly, this study extracts grid-based maps from BIM data and generates the topological maps directly through the grid-based maps using image thinning. A novel hybrid indoor map, termed Grid-Topological map, is then formed by the grid-based maps and topological maps jointly. Secondly, this study obtains the cross-floor paths from cross-floor building elements by a four-step process, namely X-Z projection, boundary extraction, X-Z topological path generation, and path-BIM intersection. Finally, experiments on eight typical types of cross-floor building elements and three multi-floor real-world buildings were conducted to prove the effectiveness of the proposed scheme, the average accuracy rates of the evaluated paths are higher than 88%. This study will advance the 3D indoor maps generation and inspire the application of indoor maps in indoor LBS, indoor robots, and 3D geographic information systems.

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Section: Grid-based Mapmentioning

confidence: 99%

Extracting 3D Indoor Maps with Any Shape Accurately Using Building Information Modeling Data

Qiu

Wang

Qing-sheng

et al. 2021

IJGI

View full text Add to dashboard Cite

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Research on the Prediction of the Operational Risk Field of Intelligent Vehicles Based on Dual Multiline LiDAR

Ruibin

Guo

Wang

et al. 2022

Journal of Advanced Transportation

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To effectively evaluate the risk situation between intelligent vehicles and surrounding traffic participants in complex scenes, a complex traffic environment perception technology based on dual multiline light detection and ranging (LiDAR) is proposed in this work. The vehicle motion state is predicted by fusing the multiview characteristics of point cloud timing and multitarget interaction information, and the risk assessment model is constructed via artificial potential field theory. The real-time point cloud information is used to obtain the time-sequence bird’s-eye view and range image. The improved VGG19 network model is used to extract the time-sequence high-level abstract combined features in the multiview scene. The constructed time-sequence feature vector is used as the input data of the attention mechanism, and the attention-bidirectional long short-term memory (Attention-BiLSTM) model is used for training to form the desired input-output mapping relationship. The motion state of the target vehicle can therefore be updated, and the static and dynamic risk fields of traffic participants surrounding the vehicle can be established based on artificial potential field theory, thereby allowing for the evaluation of the operational risk of the intelligent vehicle. The results of experiments demonstrate that the prediction effect of the target vehicle state parameters via the use of the proposed model is better than that of other compared models, and the prediction effect of the risk field of intelligent vehicle operation based on the multiview point cloud features and vehicle interaction information is good.

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One Way to Fill All the Concave Region in Grid-Based Map

Cited by 2 publications

References 25 publications

Extracting 3D Indoor Maps with Any Shape Accurately Using Building Information Modeling Data

Extracting 3D Indoor Maps with Any Shape Accurately Using Building Information Modeling Data

Research on the Prediction of the Operational Risk Field of Intelligent Vehicles Based on Dual Multiline LiDAR

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