Seminar Details
2019-06-04 (14:00) : Probabilistic modelling for sensor fusion with inertial measurements
At Euler building (room A.002)
Organized by Mathematical Engineering
Speaker :
Manon Kok (TU Delft)
Abstract :
In recent years, inertial sensors have undergone major developments. The quality of their measurements has improved while their cost has decreased, leading to an increase in availability. They can be found in stand-alone sensor units, so-called inertial measurement units, but are nowadays also present in for instance any modern smartphone, in Wii controllers and in virtual reality headsets.
The term inertial sensor refers to the combination of accelerometers and gyroscopes. They measure the external specific force and the angular velocity, respectively. Integration of their measurements provides information about the sensor's position and orientation. However, the position and orientation estimates obtained by simple integration suffer from drift and are therefore only accurate on a short time scale. In order to improve these estimates, we combine the inertial sensors with additional sensors and models. To combine these different sources of information, also called sensor fusion, we make use of probabilistic models to take the uncertainty of the different sources of information into account.
In this talk, I will discuss some of the work that I have done on the topic. First, I will discuss our approach to inertial human body motion capture, where we use data from multiple inertial sensors placed on the human body to estimate the body's position and orientation. Second, I'll discuss our work on indoor localisation where we use the magnetic field as a source of position information. We build a magnetic field map using Gaussian processes and solve the problem using simultaneous localisation and mapping (SLAM).