78 research outputs found
A Nonstochastic Information Theory for Communication and State Estimation
In communications, unknown variables are usually modelled as random
variables, and concepts such as independence, entropy and information are
defined in terms of the underlying probability distributions. In contrast,
control theory often treats uncertainties and disturbances as bounded unknowns
having no statistical structure. The area of networked control combines both
fields, raising the question of whether it is possible to construct meaningful
analogues of stochastic concepts such as independence, Markovness, entropy and
information without assuming a probability space. This paper introduces a
framework for doing so, leading to the construction of a maximin information
functional for nonstochastic variables. It is shown that the largest maximin
information rate through a memoryless, error-prone channel in this framework
coincides with the block-coding zero-error capacity of the channel. Maximin
information is then used to derive tight conditions for uniformly estimating
the state of a linear time-invariant system over such a channel, paralleling
recent results of Matveev and Savkin
Intelligent Reference Curation for Visual Place Recognition via Bayesian Selective Fusion
A key challenge in visual place recognition (VPR) is recognizing places
despite drastic visual appearance changes due to factors such as time of day,
season, weather or lighting conditions. Numerous approaches based on
deep-learnt image descriptors, sequence matching, domain translation, and
probabilistic localization have had success in addressing this challenge, but
most rely on the availability of carefully curated representative reference
images of the possible places. In this paper, we propose a novel approach,
dubbed Bayesian Selective Fusion, for actively selecting and fusing informative
reference images to determine the best place match for a given query image. The
selective element of our approach avoids the counterproductive fusion of every
reference image and enables the dynamic selection of informative reference
images in environments with changing visual conditions (such as indoors with
flickering lights, outdoors during sunshowers or over the day-night cycle). The
probabilistic element of our approach provides a means of fusing multiple
reference images that accounts for their varying uncertainty via a novel
training-free likelihood function for VPR. On difficult query images from two
benchmark datasets, we demonstrate that our approach matches and exceeds the
performance of several alternative fusion approaches along with
state-of-the-art techniques that are provided with prior (unfair) knowledge of
the best reference images. Our approach is well suited for long-term robot
autonomy where dynamic visual environments are commonplace since it is
training-free, descriptor-agnostic, and complements existing techniques such as
sequence matching.Comment: 8 pages, 10 figures, accepted in the IEEE Robotics and Automation
Letter
- …