Manon Kok
now a Associate Professor, Delft University of Technology
Publications
Using Inertial Sensors for Position and Orientation Estimation
Manon Kok, Jeroen D. Hol, Thomas B. Schön, 2017. (Foundations and Trends in Signal Processing).
Abstract▼ URL
In recent years, MEMS inertial sensors (3D accelerometers and 3D gyroscopes) have become widely available due to their small size and low cost. Inertial sensor measurements are obtained at high sampling rates and can be integrated to obtain position and orientation information. These estimates are accurate on a short time scale, but suffer from integration drift over longer time scales. To overcome this issue, inertial sensors are typically combined with additional sensors and models. In this tutorial we focus on the signal processing aspects of position and orientation estimation using inertial sensors. We discuss different modeling choices and a selected number of important algorithms. The algorithms include optimization-based smoothing and filtering as well as computationally cheaper extended Kalman filter and complementary filter implementations. The quality of their estimates is illustrated using both experimental and simulated data.
Comment: arXiv
Scalable Magnetic Field SLAM in 3D Using Gaussian Process Maps
Manon Kok, Arno Solin, July 2018. (In Proceedings of the 21th International Conference on Information Fusion (accepted for publication)). Cambridge, UK.
Abstract▼ URL
We present a method for scalable and fully 3D magnetic field simultaneous localisation and mapping (SLAM) using local anomalies in the magnetic field as a source of position information. These anomalies are due to the presence of ferromagnetic material in the structure of buildings and in objects such as furniture. We represent the magnetic field map using a Gaussian process model and take well-known physical properties of the magnetic field into account. We build local magnetic field maps using three-dimensional hexagonal block tiling. To make our approach computationally tractable we use reduced-rank Gaussian process regression in combination with a Rao–Blackwellised particle filter. We show that it is possible to obtain accurate position and orientation estimates using measurements from a smartphone, and that our approach provides a scalable magnetic SLAM algorithm in terms of both computational complexity and map storage.
On orientation estimation using iterative methods in Euclidean space
Martin A. Skoglund, Zoran Sjanic, Manon Kok, July 2017. (In Proceedings of the 20th International Conference on Information Fusion). Xi’an, China. DOI: 10.23919/ICIF.2017.8009830.
Abstract▼ URL
This paper presents three iterative methods for orientation estimation. The first two are based on iterated Extended Kalman filter (IEKF) formulations with different state representations. The first is using the well-known unit quaternion as state (q-IEKF) while the other is using orientation deviation which we call IMEKF. The third method is based on nonlinear least squares (NLS) estimation of the angular velocity which is used to parametrise the orientation. The results are obtained using Monte Carlo simulations and the comparison is done with the non-iterative EKF and multiplicative EKF (MEKF) as baseline. The result clearly shows that the IMEKF and the NLS-based method are superior to q-IEKF and all three outperform the non-iterative methods.