14,757 research outputs found
A Spectral Learning Approach to Range-Only SLAM
We present a novel spectral learning algorithm for simultaneous localization
and mapping (SLAM) from range data with known correspondences. This algorithm
is an instance of a general spectral system identification framework, from
which it inherits several desirable properties, including statistical
consistency and no local optima. Compared with popular batch optimization or
multiple-hypothesis tracking (MHT) methods for range-only SLAM, our spectral
approach offers guaranteed low computational requirements and good tracking
performance. Compared with popular extended Kalman filter (EKF) or extended
information filter (EIF) approaches, and many MHT ones, our approach does not
need to linearize a transition or measurement model; such linearizations can
cause severe errors in EKFs and EIFs, and to a lesser extent MHT, particularly
for the highly non-Gaussian posteriors encountered in range-only SLAM. We
provide a theoretical analysis of our method, including finite-sample error
bounds. Finally, we demonstrate on a real-world robotic SLAM problem that our
algorithm is not only theoretically justified, but works well in practice: in a
comparison of multiple methods, the lowest errors come from a combination of
our algorithm with batch optimization, but our method alone produces nearly as
good a result at far lower computational cost
Dense 3D Face Correspondence
We present an algorithm that automatically establishes dense correspondences
between a large number of 3D faces. Starting from automatically detected sparse
correspondences on the outer boundary of 3D faces, the algorithm triangulates
existing correspondences and expands them iteratively by matching points of
distinctive surface curvature along the triangle edges. After exhausting
keypoint matches, further correspondences are established by generating evenly
distributed points within triangles by evolving level set geodesic curves from
the centroids of large triangles. A deformable model (K3DM) is constructed from
the dense corresponded faces and an algorithm is proposed for morphing the K3DM
to fit unseen faces. This algorithm iterates between rigid alignment of an
unseen face followed by regularized morphing of the deformable model. We have
extensively evaluated the proposed algorithms on synthetic data and real 3D
faces from the FRGCv2, Bosphorus, BU3DFE and UND Ear databases using
quantitative and qualitative benchmarks. Our algorithm achieved dense
correspondences with a mean localisation error of 1.28mm on synthetic faces and
detected anthropometric landmarks on unseen real faces from the FRGCv2
database with 3mm precision. Furthermore, our deformable model fitting
algorithm achieved 98.5% face recognition accuracy on the FRGCv2 and 98.6% on
Bosphorus database. Our dense model is also able to generalize to unseen
datasets.Comment: 24 Pages, 12 Figures, 6 Tables and 3 Algorithm
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
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