Smartphone-Based 3D Indoor Localization and Navigation
Frank Ebner
Cite this publication as
Frank Ebner, Smartphone-Based 3D Indoor Localization and Navigation (2021), Logos Verlag, Berlin, ISBN: 9783832586232
659
accesses
accesses
8
quotes
quotes
Descripción / Abstract
During the last century, navigation systems have become ubiquitous and guide drivers, cyclists, and pedestrians towards their desired destinations. While operating worldwide, they rely on line-of-sight conditions towards satellites and are thus limited to outdoor areas. However, finding a gate within an airport, a ward within a hospital, or a university's auditorium also represent navigation problems. To provide navigation within such indoor environments, new approaches are required.
This thesis examines pedestrian 3D indoor localization and navigation using commodity smartphones: A desirable target platform, always at hand and equipped with a multitude of sensors. The IMU (accelerometer, gyroscope, magnetometer) and barometer allow for pedestrian dead reckoning, that is, estimating relative location changes. Absolute whereabouts can be determined via Wi-Fi, an infrastructure present within most public buildings, or by using Bluetooth Low Energy Beacons as inexpensive supplement. The building's 3D floorplan not only enables navigation, but also increases accuracy by preventing impossible movements, and serves as a visual reference for the pedestrian. All aforementioned information is fused by recursive density estimation based on a particle filter.
The conducted experiments cover both, theoretical backgrounds and real-world use-cases. All discussed approaches utilize the infrastructure found within most public buildings, are easy to set up, and maintain. Overall, this thesis results in an indoor localization and navigation system that can be easily deployed, without requiring any special hardware components.
This thesis examines pedestrian 3D indoor localization and navigation using commodity smartphones: A desirable target platform, always at hand and equipped with a multitude of sensors. The IMU (accelerometer, gyroscope, magnetometer) and barometer allow for pedestrian dead reckoning, that is, estimating relative location changes. Absolute whereabouts can be determined via Wi-Fi, an infrastructure present within most public buildings, or by using Bluetooth Low Energy Beacons as inexpensive supplement. The building's 3D floorplan not only enables navigation, but also increases accuracy by preventing impossible movements, and serves as a visual reference for the pedestrian. All aforementioned information is fused by recursive density estimation based on a particle filter.
The conducted experiments cover both, theoretical backgrounds and real-world use-cases. All discussed approaches utilize the infrastructure found within most public buildings, are easy to set up, and maintain. Overall, this thesis results in an indoor localization and navigation system that can be easily deployed, without requiring any special hardware components.
Índice
- BEGINN
- 1 Introduction
- 1.1 Navigation within Buildings
- 1.2 Research Objective
- 1.3 State of the Art
- 1.4 Scientific Contribution
- 1.5 Structure
- 2 Probabilistic Sensor Models
- 2.1 Sensor Errors
- 2.2 Probabilistic Problem Formulation
- 2.3 Global Positioning System
- 2.4 Inertial Measurement Unit
- 2.5 Barometer
- 2.6 Activity-Detection
- 2.7 Wi-Fi and Bluetooth Beacons
- 2.8 Summary
- 3 Probabilistic Movement Models
- 3.1 Probabilistic Problem Formulation
- 3.2 Simple Models without Floorplan Information
- 3.3 Simple Models with 2D Floorplan
- 3.4 Overview on Spatial Models for Indoor Floorplans
- 3.5 Regular Spatial Models for 3D Movement Prediction
- 3.6 Irregular Spatial Models for 3D Movement Prediction
- 3.7 Summary
- 4 Recursive Density Estimation
- 4.1 Probabilistic Information Fusion
- 4.2 Bayes Filter
- 4.3 Kalman Filter
- 4.4 Extended Kalman Filter
- 4.5 Particle Filter
- 4.6 Summary
- 5 Indoor Navigation
- 5.1 Complex Indoor Maps
- 5.2 Fusing All Components
- 5.3 Real-World Considerations
- 5.4 Performance Considerations
- 5.5 Summary
- 6 Experiments
- 6.1 Testbeds and Data Acquisition
- 6.2 Evaluation of Sensor Components
- 6.3 Evaluation of Movement Models
- 6.4 Evaluation of the Overall System
- 6.5 Summary
- 7 Summary
- 8 Future Work
- List of Figures
- List of Tables
- List of Symbols
- Bibliography
- Appendix
- A.1 Tilt Compensation Example
- A.2 Step-Detection Filters
- A.3 Additionally Used Maps
- A.4 Final System Results