12 research outputs found
Drift-Free Indoor Navigation Using Simultaneous Localization and Mapping of the Ambient Heterogeneous Magnetic Field
In the absence of external reference position information (e.g. GNSS) SLAM
has proven to be an effective method for indoor navigation. The positioning
drift can be reduced with regular loop-closures and global relaxation as the
backend, thus achieving a good balance between exploration and exploitation.
Although vision-based systems like laser scanners are typically deployed for
SLAM, these sensors are heavy, energy inefficient, and expensive, making them
unattractive for wearables or smartphone applications. However, the concept of
SLAM can be extended to non-optical systems such as magnetometers. Instead of
matching features such as walls and furniture using some variation of the ICP
algorithm, the local magnetic field can be matched to provide loop-closure and
global trajectory updates in a Gaussian Process (GP) SLAM framework. With a
MEMS-based inertial measurement unit providing a continuous trajectory, and the
matching of locally distinct magnetic field maps, experimental results in this
paper show that a drift-free navigation solution in an indoor environment with
millimetre-level accuracy can be achieved. The GP-SLAM approach presented can
be formulated as a maximum a posteriori estimation problem and it can naturally
perform loop-detection, feature-to-feature distance minimization, global
trajectory optimization, and magnetic field map estimation simultaneously.
Spatially continuous features (i.e. smooth magnetic field signatures) are used
instead of discrete feature correspondences (e.g. point-to-point) as in
conventional vision-based SLAM. These position updates from the ambient
magnetic field also provide enough information for calibrating the
accelerometer and gyroscope bias in-use. The only restriction for this method
is the need for magnetic disturbances (which is typically not an issue
indoors); however, no assumptions are required for the general motion of the
sensor.Comment: ISPRS Workshop Indoor 3D 201
Identifying Sources and Sinks in the Presence of Multiple Agents with Gaussian Process Vector Calculus
In systems of multiple agents, identifying the cause of observed agent
dynamics is challenging. Often, these agents operate in diverse, non-stationary
environments, where models rely on hand-crafted environment-specific features
to infer influential regions in the system's surroundings. To overcome the
limitations of these inflexible models, we present GP-LAPLACE, a technique for
locating sources and sinks from trajectories in time-varying fields. Using
Gaussian processes, we jointly infer a spatio-temporal vector field, as well as
canonical vector calculus operations on that field. Notably, we do this from
only agent trajectories without requiring knowledge of the environment, and
also obtain a metric for denoting the significance of inferred causal features
in the environment by exploiting our probabilistic method. To evaluate our
approach, we apply it to both synthetic and real-world GPS data, demonstrating
the applicability of our technique in the presence of multiple agents, as well
as its superiority over existing methods.Comment: KDD '18 Proceedings of the 24th ACM SIGKDD International Conference
on Knowledge Discovery & Data Mining, Pages 1254-1262, 9 pages, 5 figures,
conference submission, University of Oxford. arXiv admin note: text overlap
with arXiv:1709.0235
Learning unknown ODE models with Gaussian processes
In conventional ODE modelling coefficients of an equation driving the system
state forward in time are estimated. However, for many complex systems it is
practically impossible to determine the equations or interactions governing the
underlying dynamics. In these settings, parametric ODE model cannot be
formulated. Here, we overcome this issue by introducing a novel paradigm of
nonparametric ODE modelling that can learn the underlying dynamics of arbitrary
continuous-time systems without prior knowledge. We propose to learn
non-linear, unknown differential functions from state observations using
Gaussian process vector fields within the exact ODE formalism. We demonstrate
the model's capabilities to infer dynamics from sparse data and to simulate the
system forward into future.Comment: 11 pages, 2 page appendi
A spectrum of physics-informed Gaussian processes for regression in engineering
Despite the growing availability of sensing and data in general, we remain unable to fully characterize many in-service engineering systems and structures from a purely data-driven approach. The vast data and resources available to capture human activity are unmatched in our engineered world, and, even in cases where data could be referred to as “big,” they will rarely hold information across operational windows or life spans. This paper pursues the combination of machine learning technology and physics-based reasoning to enhance our ability to make predictive models with limited data. By explicitly linking the physics-based view of stochastic processes with a data-based regression approach, a derivation path for a spectrum of possible Gaussian process models is introduced and used to highlight how and where different levels of expert knowledge of a system is likely best exploited. Each of the models highlighted in the spectrum have been explored in different ways across communities; novel examples in a structural assessment context here demonstrate how these approaches can significantly reduce reliance on expensive data collection. The increased interpretability of the models shown is another important consideration and benefit in this context