38,840 research outputs found
Rhythmic Representations: Learning Periodic Patterns for Scalable Place Recognition at a Sub-Linear Storage Cost
Robotic and animal mapping systems share many challenges and characteristics:
they must function in a wide variety of environmental conditions, enable the
robot or animal to navigate effectively to find food or shelter, and be
computationally tractable from both a speed and storage perspective. With
regards to map storage, the mammalian brain appears to take a diametrically
opposed approach to all current robotic mapping systems. Where robotic mapping
systems attempt to solve the data association problem to minimise
representational aliasing, neurons in the brain intentionally break data
association by encoding large (potentially unlimited) numbers of places with a
single neuron. In this paper, we propose a novel method based on supervised
learning techniques that seeks out regularly repeating visual patterns in the
environment with mutually complementary co-prime frequencies, and an encoding
scheme that enables storage requirements to grow sub-linearly with the size of
the environment being mapped. To improve robustness in challenging real-world
environments while maintaining storage growth sub-linearity, we incorporate
both multi-exemplar learning and data augmentation techniques. Using large
benchmark robotic mapping datasets, we demonstrate the combined system
achieving high-performance place recognition with sub-linear storage
requirements, and characterize the performance-storage growth trade-off curve.
The work serves as the first robotic mapping system with sub-linear storage
scaling properties, as well as the first large-scale demonstration in
real-world environments of one of the proposed memory benefits of these
neurons.Comment: Pre-print of article that will appear in the IEEE Robotics and
Automation Letter
Featureless visual processing for SLAM in changing outdoor environments
Vision-based SLAM is mostly a solved problem providing clear, sharp images can be obtained. However, in outdoor environments a number of factors such as rough terrain, high speeds and hardware limitations can result in these conditions not being met. High speed transit on rough terrain can lead to image blur and under/over exposure, problems that cannot easily be dealt with using low cost hardware. Furthermore, recently there has been a growth in interest in lifelong autonomy for robots, which brings with it the challenge in outdoor environments of dealing with a moving sun and lack of constant artificial lighting. In this paper, we present a lightweight approach to visual localization and visual odometry that addresses the challenges posed by perceptual change and low cost cameras. The approach combines low resolution imagery with the SLAM algorithm, RatSLAM. We test the system using a cheap consumer camera mounted on a small vehicle in a mixed urban and vegetated environment, at times ranging from dawn to dusk and in conditions ranging from sunny weather to rain. We first show that the system is able to provide reliable mapping and recall over the course of the day and incrementally incorporate new visual scenes from different times into an existing map. We then restrict the system to only learning visual scenes at one time of day, and show that the system is still able to localize and map at other times of day. The results demonstrate the viability of the approach in situations where image quality is poor and environmental or hardware factors preclude the use of visual features
A hierarchical search for gravitational waves from supermassive black hole binary mergers
We present a method to search for gravitational waves from coalescing
supermassive binary black holes in LISA data. The search utilizes the
-statistic to maximize over, and determine the values of, the
extrinsic parameters of the binary system. The intrinsic parameters are
searched over hierarchically using stochastically generated multi-dimensional
template banks to recover the masses and sky locations of the binary. We
present the results of this method applied to the mock LISA data Challenge 1B
data set.Comment: 11 pages, 2 figures, for GWDAW-12 proceedings edition of CQ
Compressive Pattern Matching on Multispectral Data
We introduce a new constrained minimization problem that performs template
and pattern detection on a multispectral image in a compressive sensing
context. We use an original minimization problem from Guo and Osher that uses
minimization techniques to perform template detection in a multispectral
image. We first adapt this minimization problem to work with compressive
sensing data. Then we extend it to perform pattern detection using a formal
transform called the spectralization along a pattern. That extension brings out
the problem of measurement reconstruction. We introduce shifted measurements
that allow us to reconstruct all the measurement with a small overhead and we
give an optimality constraint for simple patterns. We present numerical results
showing the performances of the original minimization problem and the
compressed ones with different measurement rates and applied on remotely sensed
data.Comment: Published in IEEE Transactions on Geoscience and Remote Sensin
A coherent triggered search for single spin compact binary coalescences in gravitational wave data
In this paper we present a method for conducting a coherent search for single
spin compact binary coalescences in gravitational wave data and compare this
search to the existing coincidence method for single spin searches. We propose
a method to characterize the regions of the parameter space where the single
spin search, both coincident and coherent, will increase detection efficiency
over the existing non-precessing search. We also show example results of the
coherent search on a stretch of data from LIGO's fourth science run but note
that a set of signal based vetoes will be needed before this search can be run
to try to make detections.Comment: 14 pages, 4 figure
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