A Modified Methodology for Generating Indoor Navigation Models

Lewandowicz, Elżbieta; Lisowski, Przemysław; Flisek, Paweł

doi:10.3390/ijgi8020060

Cited by 17 publications

(25 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, a manual process is often costly and prone to human errors, so many studies are exploring how to automate the abovementioned process. Since the indoor space model constructed using geometric-based approaches is relatively simple, most studies focus on generation of symbolic-based models [1,5,[22][23][24][25]. Scholars divide these algorithms into three categories: visibility graph, straight skeletons, and dual structures.…”

Section: Indoor Spaces For Emergency Responsementioning

confidence: 99%

See 1 more Smart Citation

Automatic Generation of High-Accuracy Stair Paths for Straight, Spiral, and Winder Stairs Using IFC-Based Models

Lin

2020

IJGI

View full text Add to dashboard Cite

The indoor space model is the foundation of most indoor location-based services (LBS). A complete indoor space model includes floor-level paths and non-level paths. The latter includes passages connecting different floors or elevations such as stairs, elevators, escalators, and ramps. Most related studies have merely discussed the modeling and generation of floor-level paths, while those considering non-level paths usually simplify the formation and generation of non-level paths, especially stairs, which play an important role in emergency evacuation and response. Although the algorithm proposed by i-GIT approach, which considers both floor-level and non-level paths, can automatically generate paths of straight stairs, it is not applicable to the spiral stairs and winder stairs that are common in town houses and other public buildings. This study proposes a novel approach to generate high-accuracy stair paths that can support straight, spiral, and winder stairs. To implement and verify the proposed algorithm, 54 straight and spiral stairs provided by Autodesk Revit’s official website and three self-built winder stairs are used as test cases. The test results show that the algorithm can successfully produce the stair paths of most test cases (49/50), which comprehensively extends the applicability of the proposed algorithm.

show abstract

Section: Indoor Spaces For Emergency Responsementioning

confidence: 99%

“…Different models have differences in aspects like spatial topology, location reference, model accuracy, routing algorithm, and visualization of the path [15]. Therefore, there are also many scholars studying the methods of generation for different spatial data models [1,5,[22][23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

Automatic Generation of High-Accuracy Stair Paths for Straight, Spiral, and Winder Stairs Using IFC-Based Models

Lin

2020

IJGI

View full text Add to dashboard Cite

show abstract

“…Obstacles can be defined according to users' characteristics, providing a flexible solution to pathfinding. The next paper [7] focuses more specifically on the computation of navigation routes. The authors propose an original approach, producing smoother and more natural paths than other existing methods.…”

Section: Content Of the Issuementioning

confidence: 99%

Multidimensional and Multiscale GIS

Guilbert

Bogusławski

Işıkdağ

2019

IJGI

View full text Add to dashboard Cite

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.

show abstract

“…The ANNs method has several advantages over conventional NN as it is easy to use, fast to learn, provide good generalization results, has minimum inaccuracies in training and achieves minimum standard weights. Nowadays, deep learning methods are used in many areas for predictive purposes, such as deep neural networks, deep networks of faith, and recurring neural networks [ 13 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

Toward Accurate Position Estimation Using Learning to Prediction Algorithm in Indoor Navigation

Jamil

Iqbal

Ahmad

et al. 2020

Sensors

View full text Add to dashboard Cite

Internet of Things is advancing, and the augmented role of smart navigation in automating processes is at its vanguard. Smart navigation and location tracking systems are finding increasing use in the area of the mission-critical indoor scenario, logistics, medicine, and security. A demanding emerging area is an Indoor Localization due to the increased fascination towards location-based services. Numerous inertial assessments unit-based indoor localization mechanisms have been suggested in this regard. However, these methods have many shortcomings pertaining to accuracy and consistency. In this study, we propose a novel position estimation system based on learning to the prediction model to address the above challenges. The designed system consists of two modules; learning to prediction module and position estimation using sensor fusion in an indoor environment. The prediction algorithm is attached to the learning module. Moreover, the learning module continuously controls, observes, and enhances the efficiency of the prediction algorithm by evaluating the output and taking into account the exogenous factors that may have an impact on its outcome. On top of that, we reckon a situation where the prediction algorithm can be applied to anticipate the accurate gyroscope and accelerometer reading from the noisy sensor readings. In the designed system, we consider a scenario where the learning module, based on Artificial Neural Network, and Kalman filter are used as a prediction algorithm to predict the actual accelerometer and gyroscope reading from the noisy sensor reading. Moreover, to acquire data, we use the next-generation inertial measurement unit, which contains a 3-axis accelerometer and gyroscope data. Finally, for the performance and accuracy of the proposed system, we carried out numbers of experiments, and we observed that the proposed Kalman filter with learning module performed better than the traditional Kalman filter algorithm in terms of root mean square error metric.

show abstract

A Modified Methodology for Generating Indoor Navigation Models

Cited by 17 publications

References 35 publications

Automatic Generation of High-Accuracy Stair Paths for Straight, Spiral, and Winder Stairs Using IFC-Based Models

Automatic Generation of High-Accuracy Stair Paths for Straight, Spiral, and Winder Stairs Using IFC-Based Models

Multidimensional and Multiscale GIS

Toward Accurate Position Estimation Using Learning to Prediction Algorithm in Indoor Navigation

Contact Info

Product

Resources

About