Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Viset, Frida"'
Basis Function (BF) expansions are a cornerstone of any engineer's toolbox for computational function approximation which shares connections with both neural networks and Gaussian processes. Even though BF expansions are an intuitive and straightforw
Externí odkaz:
http://arxiv.org/abs/2408.07480
The Hilbert-space Gaussian Process (HGP) approach offers a hyperparameter-independent basis function approximation for speeding up Gaussian Process (GP) inference by projecting the GP onto M basis functions. These properties result in a favorable dat
Externí odkaz:
http://arxiv.org/abs/2408.02346
Publikováno v:
26th International Conference on Information Fusion, Charleston, SC, USA, June 2023
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 measurements of position and orientation can cause the integrated position est
Externí odkaz:
http://arxiv.org/abs/2310.19400
Spatially scalable recursive estimation of Gaussian process terrain maps using local basis functions
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 estimates when the agent returns to a previously mapped area. Mapping algorith
Externí odkaz:
http://arxiv.org/abs/2210.09168
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 algorithm uses two maps, namely, a motion map and a magnetic field ma
Externí odkaz:
http://arxiv.org/abs/2203.15866
Autor:
Viset, Frida1 (AUTHOR) f.m.viset@tudelft.nl, Helmons, Rudy2 (AUTHOR) r.l.j.helmons@tudelft.nl, Kok, Manon1 (AUTHOR)
Publikováno v:
Sensors (14248220). Apr2022, Vol. 22 Issue 8, pN.PAG-N.PAG. 19p.
In order to perform GP predictions fast in large geospatial fields with small-scale variations, a computational complexity that is independent of the number of measurements $N$ and the size of the field is crucial. In this setting, GP approximations
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2f9fcc077893d6122ace4d64d11da68a