Learning target dynamics while tracking using Gaussian processes
C Veibäck, J Olofsson, TR Lauknes… - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
Tracked targets often exhibit common behaviors due to influences from the surrounding
environment, such as wind or obstacles, which are usually modeled as noise. Here, these …
environment, such as wind or obstacles, which are usually modeled as noise. Here, these …
Trajectory planning for unmanned surface vehicles operating under wave-induced motion uncertainty in dynamic environments
P Rajendran, T Moscicki, J Wampler… - International …, 2020 - journals.sagepub.com
We present a deliberative trajectory planning method to avoid collisions with traffic vessels. It
also plans traversal across wavefields generated by these vessels and minimizes the risk of …
also plans traversal across wavefields generated by these vessels and minimizes the risk of …
Modeling and classification of trajectories based on a gaussian process decomposition into discrete components
We present a method to model and classify trajectory data that come from surveillance
videos. Observations of the locations of moving entities are used to estimate their expected …
videos. Observations of the locations of moving entities are used to estimate their expected …
Towards unsupervised learning, classification and prediction of activities in a stream-based framework
Learning to recognize common activities such as traffic activities and robot behavior is an
important and challenging problem related both to AI and robotics. We propose an …
important and challenging problem related both to AI and robotics. We propose an …
Enhancing lattice-based motion planning with introspective learning and reasoning
M Tiger, D Bergström, A Norrstig… - IEEE Robotics and …, 2021 - ieeexplore.ieee.org
Lattice-based motion planning is a hybrid planning method where a plan is made up of
discrete actions, while simultaneously also being a physically feasible trajectory. The …
discrete actions, while simultaneously also being a physically feasible trajectory. The …
Gaussian Process for Trajectories
4.1 The availability of devices with location tracking capability has helped generate a
tremendous amount of trajectory data of humans, animals, vehicles, and drones, which is …
tremendous amount of trajectory data of humans, animals, vehicles, and drones, which is …
Gaussian Process Regression-based GPS Variance Estimation and Trajectory Forecasting
L Kortesalmi - 2018 - diva-portal.org
Spatio-temporal data is a commonly used source of information. Using machine learning to
analyse this kind of data can lead to many interesting and useful insights. In this thesis …
analyse this kind of data can lead to many interesting and useful insights. In this thesis …
Modeling RTK-GNSS Trajectory Data using Sparse Gaussian Process Models
The Gaussian process regression (GPR) has been applied to model trajectory points from
Global Navigation Satellite System (GNSS). Trajectory modeling using GP in previous works …
Global Navigation Satellite System (GNSS). Trajectory modeling using GP in previous works …
Conceptual framework for the analysis and modeling of GNSS measurements based on Gaussian process
RS Nahar, KM Ng, N Abdul Razak… - Journal of Electrical and …, 2021 - ir.uitm.edu.my
The Global Navigation Satellite Systems (GNSS) have been used in autonomous vehicles
and remote sensing. The GNSS receiver can be a conventional device or an enhanced …
and remote sensing. The GNSS receiver can be a conventional device or an enhanced …
Speeding Up Trajectory Planning for Autonomous Robots Operating in Complex Environments
P Rajendran - 2019 - search.proquest.com
Advances in sensing and computing hardware have physically equipped robots to operate
in complex environments. In many real-world settings, we desire robots to operate at a high …
in complex environments. In many real-world settings, we desire robots to operate at a high …