Search Results for author:
Found 13 papers, 4 papers with code
Projecting basis functions with tensor networks for Gaussian process regression
The benefit of our approach comes from the projection to a smaller subspace: It modifies the shape of the basis functions in a way that it sees fit based on the given data, and it allows for efficient computations in the smaller subspace.
Mapping the magnetic field using a magnetometer array with noisy input Gaussian process regression
In this paper, we investigate how an array of magnetometers can be used to improve the quality of the magnetic field map.
Large-scale magnetic field maps using structured kernel interpolation for Gaussian process regression
Because full GP regression has a complexity that grows cubically with the number of data points, approximations for GPs have been extensively studied.
Tightly Integrated Motion Classification and State Estimation in Foot-Mounted Navigation Systems
A framework for tightly integrated motion mode classification and state estimation in motion-constrained inertial navigation systems is presented.
Spatially scalable recursive estimation of Gaussian process terrain maps using local basis functions
As our proposed algorithm is recursive, it can naturally be incorporated into existing algorithms that uses Gaussian process maps for SLAM.
Indoor SLAM Using a Foot-mounted IMU and the local Magnetic Field
The algorithm uses two maps, namely, a motion map and a magnetic field map.
Observability of the relative motion from inertial data in kinematic chains
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 applications.
Alternating linear scheme in a Bayesian framework for low-rank tensor approximation
Multiway data often naturally occurs in a tensorial format which can be approximately represented by a low-rank tensor decomposition.
Know Your Boundaries: Constraining Gaussian Processes by Variational Harmonic Features
Gaussian processes (GPs) provide a powerful framework for extrapolation, interpolation, and noise removal in regression and classification.
Scalable Magnetic Field SLAM in 3D Using Gaussian Process Maps
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.
Using Inertial Sensors for Position and Orientation Estimation
In this tutorial we focus on the signal processing aspects of position and orientation estimation using inertial sensors.
Robotics Systems and Control
Modeling and interpolation of the ambient magnetic field by Gaussian processes
Anomalies in the ambient magnetic field can be used as features in indoor positioning and navigation.
Newton-based maximum likelihood estimation in nonlinear state space models
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 and its gradient and Hessian.
