346 citations · 656 across the 19 of their papers we have counts for
21 papers
Distributed multi-agent magnetic field norm SLAM with Gaussian processes
Frida Viset, Rudy Helmons, Manon Kok
Accurately estimating the positions of multi-agent systems in indoor environments is challenging due to the lack of Global Navigation Satelite System (GNSS) signals. Noisy measurem…
Mapping the magnetic field using a magnetometer array with noisy input Gaussian process regression
Thomas Edridge, Manon Kok
Ferromagnetic materials in indoor environments give rise to disturbances in the ambient magnetic field. Maps of these magnetic disturbances can be used for indoor localisation. A G…
Large-scale magnetic field maps using structured kernel interpolation for Gaussian process regression
Clara Menzen, Marnix Fetter, Manon Kok
We present a mapping algorithm to compute large-scale magnetic field maps in indoor environments with approximate Gaussian process (GP) regression. Mapping the spatial variations i…
Rao-Blackwellized Particle Smoothing for Simultaneous Localization and Mapping
Manon Kok, Arno Solin, Thomas B. Schön
Simultaneous localization and mapping (SLAM) is the task of building a map representation of an unknown environment while at the same time using it for positioning. A probabilistic…
Tightly Integrated Motion Classification and State Estimation in Foot-Mounted Navigation Systems
Isaac Skog, Gustaf Hendeby, Manon Kok
A framework for tightly integrated motion mode classification and state estimation in motion-constrained inertial navigation systems is presented. The framework uses a jump Markov…
Spatially scalable recursive estimation of Gaussian process terrain maps using local basis functions
Frida Marie Viset, Rudy Helmons, Manon Kok
When an agent, person, vehicle or robot is moving through an unknown environment without GNSS signals, online mapping of nonlinear terrains can be used to improve position estimate…
Indoor SLAM Using a Foot-mounted IMU and the local Magnetic Field
Mostafa Osman, Frida Viset, Manon Kok
In this paper, a simultaneous localization and mapping (SLAM) algorithm for tracking the motion of a pedestrian with a foot-mounted inertial measurement unit (IMU) is proposed. The…
Observability of the relative motion from inertial data in kinematic chains
Manon Kok, Karsten Eckhoff, Ive Weygers +1
Real-time motion tracking of kinematic chains is a key prerequisite in the control of, e.g., robotic actuators and autonomous vehicles and also has numerous biomechanical applicati…
Alternating linear scheme in a Bayesian framework for low-rank tensor approximation
Clara Menzen, Manon Kok, Kim Batselier
Multiway data often naturally occurs in a tensorial format which can be approximately represented by a low-rank tensor decomposition. This is useful because complexity can be signi…
Sensor Artificial Intelligence and its Application to Space Systems -- A White Paper
Anko Börner, Heinz-Wilhelm Hübers, Odej Kao +15
Information and communication technologies have accompanied our everyday life for years. A steadily increasing number of computers, cameras, mobile devices, etc. generate more and…
Sparse Magnetometer-free Inertial Motion Tracking -- A Condition for Observability in Double Hinge Joint Systems
Karsten Eckhoff, Manon Kok, Sergio Lucia +1
Inertial measurement units are commonly used in a growing number of application fields to track or capture motions of kinematic chains, such as human limbs, exoskeletons or robotic…
A Fast and Robust Algorithm for Orientation Estimation using Inertial Sensors
Manon Kok, Thomas B. Schön
We present a novel algorithm for online, real-time orientation estimation. Our algorithm integrates gyroscope data and corrects the resulting orientation estimate for integration d…
Sensor Fusion for Magneto-Inductive Navigation
Johan Wahlström, Manon Kok, Pedro Porto Buarque de Gusmao +3
Magneto-inductive navigation is an inexpensive and easily deployable solution to many of today's navigation problems. By utilizing very low frequency magnetic fields, magneto-induc…
Know Your Boundaries: Constraining Gaussian Processes by Variational Harmonic Features
Arno Solin, Manon Kok
Gaussian processes (GPs) provide a powerful framework for extrapolation, interpolation, and noise removal in regression and classification. This paper considers constraining GPs to…
Scalable Magnetic Field SLAM in 3D Using Gaussian Process Maps
Manon Kok, Arno Solin
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 inform…
Using Inertial Sensors for Position and Orientation Estimation
Manon Kok, Jeroen D. Hol, Thomas B. Schön
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 obt…
A Scalable and Distributed Solution to the Inertial Motion Capture Problem
Manon Kok, Sina Khoshfetrat Pakazad, Thomas B. Schön +2
In inertial motion capture, a multitude of body segments are equipped with inertial sensors, consisting of 3D accelerometers and 3D gyroscopes. Using an optimization-based approach…
Magnetometer calibration using inertial sensors
Manon Kok, Thomas B. Schön
In this work we present a practical algorithm for calibrating a magnetometer for the presence of magnetic disturbances and for magnetometer sensor errors. To allow for combining th…
Modeling and interpolation of the ambient magnetic field by Gaussian processes
Arno Solin, Manon Kok, Niklas Wahlström +2
Anomalies in the ambient magnetic field can be used as features in indoor positioning and navigation. By using Maxwell's equations, we derive and present a Bayesian non-parametric…
Nonlinear state space smoothing using the conditional particle filter
Andreas Svensson, Thomas B. Schön, Manon Kok
To estimate the smoothing distribution in a nonlinear state space model, we apply the conditional particle filter with ancestor sampling. This gives an iterative algorithm in a Mar…
Newton-based maximum likelihood estimation in nonlinear state space models
Manon Kok, Johan Dahlin, Thomas B. Schön +1
Maximum likelihood (ML) estimation using Newton's method in nonlinear state space models (SSMs) is a challenging problem due to the analytical intractability of the log-likelihood…