17,642 research outputs found
The effect of magnetic dipolar interactions on the interchain spin wave dispersion in CsNiF_3
Inelastic neutron scattering measurements were performed on the ferromagnetic
chain system CsNiF_3 in the collinear antiferromagnetic ordered state below T_N
= 2.67K. The measured spin wave dispersion was found to be in good agreement
with linear spin wave theory including dipolar interactions. The additional
dipole tensor in the Hamiltonian was essential to explain some striking
phenomena in the measured spin wave spectrum: a peculiar feature of the
dispersion relation is a jump at the zone center, caused by strong dipolar
interactions in this system. The interchain exchange coupling constant and the
planar anisotropy energy were determined within the present model to be J'/k_B
= -0.0247(12)K and A/k_B = 3.3(1)K. This gives a ratio J/J' \approx 500, using
the previously determined intrachain coupling constant J/k_B = 11.8$. The small
exchange energy J' is of the same order as the dipolar energy, which implies a
strong competition between the both interactions.Comment: 18 pages, TeX type, 7 Postscript figures included. To be published in
Phys. Rev.
Efficient Constellation-Based Map-Merging for Semantic SLAM
Data association in SLAM is fundamentally challenging, and handling ambiguity
well is crucial to achieve robust operation in real-world environments. When
ambiguous measurements arise, conservatism often mandates that the measurement
is discarded or a new landmark is initialized rather than risking an incorrect
association. To address the inevitable `duplicate' landmarks that arise, we
present an efficient map-merging framework to detect duplicate constellations
of landmarks, providing a high-confidence loop-closure mechanism well-suited
for object-level SLAM. This approach uses an incrementally-computable
approximation of landmark uncertainty that only depends on local information in
the SLAM graph, avoiding expensive recovery of the full system covariance
matrix. This enables a search based on geometric consistency (GC) (rather than
full joint compatibility (JC)) that inexpensively reduces the search space to a
handful of `best' hypotheses. Furthermore, we reformulate the commonly-used
interpretation tree to allow for more efficient integration of clique-based
pairwise compatibility, accelerating the branch-and-bound max-cardinality
search. Our method is demonstrated to match the performance of full JC methods
at significantly-reduced computational cost, facilitating robust object-based
loop-closure over large SLAM problems.Comment: Accepted to IEEE International Conference on Robotics and Automation
(ICRA) 201
Complexity Analysis and Efficient Measurement Selection Primitives for High-Rate Graph SLAM
Sparsity has been widely recognized as crucial for efficient optimization in
graph-based SLAM. Because the sparsity and structure of the SLAM graph reflect
the set of incorporated measurements, many methods for sparsification have been
proposed in hopes of reducing computation. These methods often focus narrowly
on reducing edge count without regard for structure at a global level. Such
structurally-naive techniques can fail to produce significant computational
savings, even after aggressive pruning. In contrast, simple heuristics such as
measurement decimation and keyframing are known empirically to produce
significant computation reductions. To demonstrate why, we propose a
quantitative metric called elimination complexity (EC) that bridges the
existing analytic gap between graph structure and computation. EC quantifies
the complexity of the primary computational bottleneck: the factorization step
of a Gauss-Newton iteration. Using this metric, we show rigorously that
decimation and keyframing impose favorable global structures and therefore
achieve computation reductions on the order of and , respectively,
where is the pruning rate. We additionally present numerical results
showing EC provides a good approximation of computation in both batch and
incremental (iSAM2) optimization and demonstrate that pruning methods promoting
globally-efficient structure outperform those that do not.Comment: Pre-print accepted to ICRA 201
Comparing persistence diagrams through complex vectors
The natural pseudo-distance of spaces endowed with filtering functions is
precious for shape classification and retrieval; its optimal estimate coming
from persistence diagrams is the bottleneck distance, which unfortunately
suffers from combinatorial explosion. A possible algebraic representation of
persistence diagrams is offered by complex polynomials; since far polynomials
represent far persistence diagrams, a fast comparison of the coefficient
vectors can reduce the size of the database to be classified by the bottleneck
distance. This article explores experimentally three transformations from
diagrams to polynomials and three distances between the complex vectors of
coefficients.Comment: 11 pages, 4 figures, 2 table
The Development of Learning Tasks for those Central Washington State College Freshmen Prospective Teacher Candidates Participating in Field Experiences in the Ellensburg Public Schools
It was the purpose of this study to describe and to explore the factors in the development and the planning and organization of appropriate learning tasks in the field experience for the Central Washington State College Pre-Professional Students within the Ellensburg Public Schools.
The new experience for these freshmen raised the question: What are appropriate learning tasks or competencies that these freshmen should or should not have? Thus this study: (1) examined various learning tasks; (2) developed a rationale for appropriate competencies; and (3) developed and listed appropriate tasks in behavioral terms for these freshmen
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