Indoor SLAM using WiFi RTT ranging and visual-inertial dead-reckoning data
Salah, Amir Ismail (2020)
2020
Master's Programme in Information Technology
Informaatioteknologian ja viestinnän tiedekunta - Faculty of Information Technology and Communication Sciences
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Hyväksymispäivämäärä
2020-11-24
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:tuni-202010287653
https://urn.fi/URN:NBN:fi:tuni-202010287653
Tiivistelmä
In recent years, the need for highly accurate indoor positioning technologies and services has seen a huge and continuously growing demand. While satellite positioning can be accurate and has been the industry standard for positioning outdoor globally, the technologies associated with it are simply not available on a large scale indoor. With the ongoing development of hardware and software-based positioning technologies such as UWB, RTT, and Bluetooth based positioning, indoor positioning is gathering more interest due to its importance for connecting people and IoT devices in the future.
In this thesis, the focus is on exploring the SLAM implementation for an indoor positioning system using mainly round-trip-time measurements, mobile inertial measurement unit readings, and visual features with ARCore and the theoretical and mathematical background on multiple positioning topics and algorithms used in different SLAM approaches. First, the problem is presented as to what exactly we are trying to solve, theoretical background follows with the mathematical details. The practical part is then later discussed, with further explanation on the different implementation choices made.
The end of this thesis presents the results achieved by the different SLAM techniques in our implementation. Furthermore, the challenges met in the development phase are discussed.
In this thesis, the focus is on exploring the SLAM implementation for an indoor positioning system using mainly round-trip-time measurements, mobile inertial measurement unit readings, and visual features with ARCore and the theoretical and mathematical background on multiple positioning topics and algorithms used in different SLAM approaches. First, the problem is presented as to what exactly we are trying to solve, theoretical background follows with the mathematical details. The practical part is then later discussed, with further explanation on the different implementation choices made.
The end of this thesis presents the results achieved by the different SLAM techniques in our implementation. Furthermore, the challenges met in the development phase are discussed.