pub_proc.bib
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@inproceedings{censi11semantics,
author = {Andrea Censi and Richard M. Murray},
title = {Uncertain semantics, representation nuisances, and necessary invariance properties of bootstrapping agents},
booktitle = {Joint IEEE International Conference on Development and Learning
and Epigenetic Robotics},
year = {2011},
month = {Aug},
pdf = {http://purl.org/censi/research/2011-icdl-invariance.pdf}
}
@inproceedings{scaramuzza11visodo,
author = {Davide Scaramuzza and Andrea Censi and Kostas Daniilidis},
title = {Exploiting motion priors in visual odometry for vehicle-mounted cameras with non-holonomic constraints},
booktitle = {{IEEE/RSJ} International Conference on Intelligent Robots and Systems (IROS)},
year = {2011},
month = {Sep},
pdf = {http://purl.org/censi/research/2011-iros-scaRansac.pdf}
}
@inproceedings{censi11bgds,
author = {Andrea Censi and Richard M. Murray},
title = {Bootstrapping sensorimotor cascades: a group-theoretic perspective},
booktitle = {{IEEE/RSJ} International Conference on Intelligent Robots and Systems (IROS)},
year = {2011},
month = {Sep},
pdf = {http://purl.org/censi/research/2011-iros-bgds.pdf},
url = {http://purl.org/censi/2011/bgds}
}
@inproceedings{censi11bds,
author = {Andrea Censi and Richard M. Murray},
title = {Bootstrapping bilinear models of robotic sensorimotor cascades},
booktitle = {Proceedings of the {IEEE} International Conference
on Robotics and Automation ({ICRA})},
year = {2011},
month = {May},
url = {http://purl.org/censi/2010/boot},
pdf = {http://purl.org/censi/research/2010-bootstrapping-bilinear-short.pdf},
slides = {http://purl.org/censi/research/2011-icra-bds-slides.pdf}
}
@article{censi11kf,
author = {Censi, Andrea},
journal = {IEEE Transactions on Automatic Control},
title = {Kalman filtering with intermittent observations: convergence for semi-Markov chains and an intrinsic performance measure},
year = {2011},
month = {February},
doi = {10.1109/TAC.2010.2097350},
issn = {0018-9286},
pdf = {http://purl.org/censi/research/2011-tac-kf-geometry.pdf}
}
@inproceedings{han10pose,
author = {Shuo Han and Andrea Censi and Andrew D. Straw and Richard M. Murray},
booktitle = {{IEEE/RSJ} International Conference on Intelligent Robots and Systems (IROS)},
title = {A bio-plausible design for visual pose stabilization},
year = {2010},
pages = {5679--5686},
abstract = {We consider the problem of purely visual pose stabilization (also known as servoing) of a second-order rigid-body system with six degrees of freedom: how to choose forces and torques, based on the current view and a memorized goal image, to steer the pose towards a desired one. Emphasis has been given to the bio-plausibility of the computation, in the sense that the control laws could be in principle implemented on the neural substrate of simple insects. We show that stabilizing laws can be realized by bilinear/quadratic operations on the visual input. This particular computational structure has several numerically favorable characteristics (sparse, local, and parallel), and thus permits an efficient engineering implementation. We show results of the control law tested on an indoor helicopter platform.},
keywords = {bilinear operation;quadratic operation;second-order rigid-body system;visual pose stabilization;visual servoing;bilinear systems;biophysics;control system synthesis;mobile robots;path planning;pose estimation;stability;visual servoing;},
doi = {10.1109/IROS.2010.5652857},
video = {http://purl.org/hanshuo/2010/pd_pose_stabilization},
slides = {http://purl.org/censi/research/2009-cdc-bio-attitude-slides.pdf}
}
@inproceedings{censi09attitude,
author = { Andrea Censi and Shuo Han and Sawyer B. Fuller and Richard M. Murray},
booktitle = {Proceedings of the 48th IEEE Conference on Decision and Control},
title = {A bio-plausible design for visual attitude stabilization},
year = {2009},
keywords = {PD controller;Reichardt correlators;bilinear computation;bioplausible design;current visual input;delayed visual input;lobula plate tangential cells;neural networks algorithms;visual attitude stabilization;visual information processing;visual sensory input;PD control;biocontrol;delays;image processing;neurocontrollers;},
doi = {10.1109/CDC.2009.5400408},
pdf = {http://purl.org/censi/research/2009-cdc-bio-attitude.pdf},
slides = {http://purl.org/censi/research/2009-cdc-bio-attitude-slides.pdf}
}
@inproceedings{censi09fractals,
author = {Andrea Censi},
title = {On the performance of {K}alman filtering with intermittent observations: a geometric approach with fractals},
booktitle = {Proceedings of the American Control Conference (ACC)},
year = {2009},
url = {http://purl.org/censi/2008/fractals},
pdf = {http://purl.org/censi/research/2009-acc-fractals.pdf},
slides = {http://purl.org/censi/research/2009-acc-fractals-slides.pdf},
abstract = {
This paper describes the stationary distribution of the a-posteriori
covariance matrix of a Kalman filter when the availability of measurements
is subject to random phenomena such as lossy network links.
If a certain non-overlapping condition is satisfied, the cdf has a fractal nature,
and there exists a closed-form expression for it. If the condition
is not satisfied, deciding whether the cdf is singular or not, even
in the scalar case, is at least as hard as some open problems in measure and number theory.
}
}
@inproceedings{censi09posetracking,
author = {Andrea Censi},
title = {On achievable accuracy for pose tracking},
booktitle = {Proceedings of the {IEEE} International Conference
on Robotics and Automation ({ICRA})},
year = {2009},
url = {http://purl.org/censi/2008/posetracking},
pdf = {http://purl.org/censi/research/2009-icra-posetracking.pdf},
slides = {http://purl.org/censi/research/2009-icra-posetracking-slides.pdf},
abstract = {
This paper presents Cramer-Rao bound-like inequalities for
pose tracking, which is defined as the problem of
recovering the robot displacement given two successive
readings of a relative sensor. Computing the exact Fisher
Information Matrix (FIM) for pose tracking is hard, because
the state comprises the map, which is infinite-dimensional
and unknown. This paper shows that the FIM for pose
tracking can be bounded by a function of the FIM for
localization on a known map, thereby reducing the analysis
to a finite-dimensional problem. The resulting bounds are
independent of the map prior and representation. The
results are valid for any relative sensor; the experimental
verification is done for the particular case of pose
tracking using range-finders (scan matching).
}
}
@inproceedings{carpin09hsm3d,
author = {Carpin, Stefano and Censi, Andrea},
booktitle = {Proceedings of the {IEEE/RSJ} International Conference on Intelligent Robots and Systems (IROS)},
title = {An experimental assessment of the HSM3D algorithm for sparse and colored data},
year = {2009},
pages = {3595-3600},
keywords = {HSM3D algorithm;mobile robots;point cloud matching;six dimensional scan-matching problem;stereo cameras;cameras;image color analysis;image matching;mobile robots;stereo image processing;},
doi = {10.1109/IROS.2009.5354618}
}
@inproceedings{censi09hsm3d,
author = {Andrea Censi and Stefano Carpin},
title = {{HSM3D}: Feature-Less Global {6DOF} Scan-Matching in the {H}ough/{R}adon Domain },
booktitle = {Proceedings of the {IEEE} International Conference
on Robotics and Automation ({ICRA})},
year = {2009},
url = {http://purl.org/censi/2008/hsm3d},
pdf = {http://purl.org/censi/research/2009-icra-hsm3d.pdf},
slides = {http://purl.org/censi/research/2009-icra-hsm3d-slides.pdf},
abstract = {
This paper presents HSM3D, an algorithm for global rigid
6DOF alignment of 3D point clouds. The algorithm works by
projecting the two input sets into the Radon/Hough domain,
whose properties allow to decompose the 6DOF search into a
series of fast one-dimensional cross-correlations. No
planes or other particular features must be present in the
input data, and the algorithm is provably complete in the
case of noise-free input. The algorithm has been
experimentally validated on publicly available data sets.
}
}
@inproceedings{censi09consensus,
author = {Andrea Censi and Richard M. Murray},
title = {Real-valued consensus over noisy quantized channels},
booktitle = {Proceedings of the American Control Conference (ACC)},
year = {2009},
url = {http://purl.org/censi/2008/consensus},
pdf = {http://purl.org/censi/research/2009-acc-consensus.pdf},
slides = {http://purl.org/censi/research/2009-acc-consensus-slides.pdf},
abstract = {
This paper concerns the average consensus problem with the
constraint of quantized communication between nodes. A
broad class of algorithms is analyzed, in which the
transmission strategy, which decides what value to
communicate to the neighbors, can include various kinds of
rounding, probabilistic quantization, and bounded noise.
The arbitrariness of the transmission strategy is
compensated by a feedback mechanism which can be
interpreted as a self-inhibitory action. The result is that
the average of the nodes state is not conserved across
iterations, and the nodes do not converge to a consensus;
however, we show that both errors can be made as small as
desired. Bounds on these quantities involve the spectral
properties of the graph and can be proved by employing
elementary techniques of LTI systems analysis.
}
}
@inproceedings{calisi08openrdk,
author = {Daniele Calisi and Andrea Censi and Luca Iocchi and Daniele Nardi},
title = {{OpenRDK}: A Modular Framework for Robotic Software Development },
booktitle = {Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
year = {2008},
month = {September},
address = {Nice, France},
url = {http://openrdk.sourceforge.net},
pdf = {http://purl.org/censi/research/2008-iros-openrdk.pdf},
slides = {http://purl.org/censi/research/2008-iros-openrdk-slides.pdf},
abstract = {
In this paper we conduct an analysis of existing
frameworks for robot software development and we
present OpenRDK, a modular framework focused on
rapid development of distributed robotic systems.
It has been designed following users' advice and
has been in use within our group for several
years. By now OpenRDK has been successfully
applied in diverse applications with heterogeneous
robots and as we believe it is fruitfully usable
by others we are releasing it as open source..
}
}
@inproceedings{censi08ppu,
author = {Andrea Censi and Daniele Calisi and
Alessandro De Luca and Giuseppe Oriolo},
title = {A {B}ayesian framework for optimal motion
planning with uncertainty },
booktitle = {Proceedings of the {IEEE} International Conference
on Robotics and Automation ({ICRA})},
year = {2008},
month = {May},
address = {Pasadena, CA},
doi = {10.1109/ROBOT.2008.4543469},
url = {http://purl.org/censi/2007/ppu},
pdf = {http://purl.org/censi/research/2008-icra-ppu.pdf},
slides = {http://purl.org/censi/research/2008-icra-ppu-slides.pdf},
abstract = {
Modeling robot motion planning with uncertainty in a
Bayesian framework leads to a computationally intractable
stochastic control problem. We seek hypotheses that can
justify a separate implementation of control, localization
and planning. In the end, we reduce the stochastic control
problem to path-planning in the extended space of poses x
covariances; the transitions between states are modeled
through the use of the Fisher information matrix. In this
framework, we consider two problems: minimizing the
execution time, and minimizing the final covariance, with
an upper bound on the execution time. Two correct and
complete algorithms are presented. The first is the direct
extension of classical graph-search algorithms in the
extended space. The second one is a back-projection
algorithm: uncertainty constraints are propagated backward
from the goal towards the start state.
}
}
@inproceedings{censi08plicp,
author = {Andrea Censi},
title = {An {ICP} variant using a point-to-line metric},
booktitle = {Proceedings of the {IEEE} International Conference
on Robotics and Automation ({ICRA})},
year = {2008},
month = {May},
address = {Pasadena, CA},
doi = {10.1109/ROBOT.2008.4543181},
url = {http://purl.org/censi/2007/plicp},
pdf = {http://purl.org/censi/research/2008-icra-plicp.pdf},
slides = {http://purl.org/censi/research/2008-icra-plicp-slides.pdf},
abstract = {
This paper describes PLICP, an ICP (Iterative
Closest/Corresponding Point) variant that uses a
point-to-line metric, and an exact closed-form for
minimizing such metric. The resulting algorithm has some
interesting properties: it converges quadratically, and in
a finite number of steps. The method is validated against
vanilla ICP, IDC (Iterative Dual Correspondences), and
MbICP (Metric-Based ICP) by reproducing the experiments
performed in Minguez et al. (2006). The experiments suggest
that PLICP is more precise, and requires less iterations.
However, it is less robust to very large initial
displacement errors. The last part of the paper is devoted
to purely algorithmic optimization of the correspondence
search; this allows for significant speed-up of the
computation. The source code is available for download.
}
}
@inproceedings{censi08fts,
author = {Andrea Censi and Gian Diego Tipaldi},
title = {Lazy Localization using the {F}rozen-{T}ime {S}moother},
booktitle = {Proceedings of the {IEEE} International Conference
on Robotics and Automation ({ICRA})},
year = {2008},
month = {May},
address = {Pasadena, CA},
doi = {10.1109/ROBOT.2008.4543631},
url = {http://purl.org/censi/2007/fts},
pdf = {http://purl.org/censi/research/2008-icra-fts.pdf},
abstract = {
We present a new algorithm for solving the global localization
problem called Frozen-Time Smoother (FTS). Time is `frozen', in
the sense that the belief always refers to the same time instant,
instead of following a moving target, like Monte Carlo
Localization does. This algorithm works in the case in which
global localization is formulated as a smoothing problem, and a
precise estimate of the incremental motion of the robot is
usually available. These assumptions correspond to the case when
global localization is used to solve the loop closing problem in
SLAM. We compare FTS to two Monte Carlo methods designed with the
same assumptions. The experiments suggest that a naive
implementation of the FTS is more efficient than an extremely
optimized equivalent Monte Carlo solution. Moreover, the FTS has
an intrinsic laziness: it does not need frequent updates (scans
can be integrated once every many meters) and it can process data
in arbitrary order. The source code and datasets are available
for download.
}
}
@inproceedings{censi08calib,
author = {Andrea Censi and Luca Marchionni and Giuseppe Oriolo},
title = { Simultaneous maximum-likelihood calibration of robot and sensor parameters},
booktitle = {Proceedings of the {IEEE} International Conference
on Robotics and Automation ({ICRA})},
year = {2008},
month = {May},
address = {Pasadena, CA},
doi = {10.1109/ROBOT.2008.4543516},
url = {http://purl.org/censi/2007/calib},
pdf = {http://purl.org/censi/research/2008-icra-calibration.pdf},
slides = {http://purl.org/censi/research/2008-icra-calibration-slides.pdf},
video = {http://purl.org/censi/research/2008-icra-calibration-video.mpg},
abstract = {
For a differential-drive mobile robot equipped with an
on-board range sensor, there are six parameters to
calibrate: three for the odometry (radii and distance
between the wheels), and three for the pose of the sensor
with respect to the robot frame. This paper describes a
method for calibrating all six parameters at the same time,
without the need for external sensors or devices. Moreover,
it is not necessary to drive the robot along particular
trajectories. The available data are the measures of the
angular velocities of the wheels and the range sensor
readings. The maximum-likelihood calibration solution is
found in a closed form.
}
}
@inproceedings{censi07accurate,
author = {Andrea Censi},
title = {An accurate closed-form estimate of {ICP}'s covariance},
booktitle = {Proceedings of the {IEEE} International Conference
on Robotics and Automation ({ICRA})},
pages = {3167--3172},
year = {2007},
month = {April},
address = {Rome, Italy},
doi = {10.1109/ROBOT.2007.363961},
issn = {1050-4729},
url = {http://purl.org/censi/2006/icpcov},
pdf = {http://purl.org/censi/research/2007-icra-icpcov.pdf},
slides = {http://purl.org/censi/research/2007-icra-icpcov-slides.pdf},
abstract = {
Existing methods for estimating the covariance of the ICP
(Iterative Closest/Corresponding Point) algorithm are either
inaccurate or are computationally too expensive to be used online.
This paper proposes a new method, based on the analysis of the error
function being minimized. It considers that the correspondences are
not independent (the same measurement being used in more than one correspondence),
and explicitly utilizes the covariance matrix of the measurements,
which are not assumed to be independent either. The validity of the
approach is verified through extensive simulations: it is more accurate
than previous methods and its computational load is negligible.
The ill-posedness of the surface matching problem is explicitly tackled
for under-constrained situations by performing an observability analysis;
in the analyzed cases the method still provides a good estimate of the
error projected on the observable manifold.
}
}
@inproceedings{censi07achievable,
author = {Andrea Censi},
title = {On achievable accuracy
for range-finder localization},
booktitle = {Proceedings of the {IEEE} International Conference
on Robotics and Automation ({ICRA})},
address = {Rome, Italy},
year = {2007},
month = apr,
pages = {4170--4175},
doi = {10.1109/ROBOT.2007.364120},
issn = {1050-4729},
url = {http://purl.org/censi/2006/accuracy},
pdf = {http://purl.org/censi/research/2007-icra-accuracy.pdf},
slides = {http://purl.org/censi/research/2007-icra-accuracy-slides.pdf},
abstract = {
The covariance of every unbiased estimator is bounded by the Cramer-Rao
lower bound, which is the inverse of Fisher's information matrix.
This paper shows that, for the case of localization with range-finders,
Fisher's matrix is a function of the expected readings and of the
orientation of the environment's surfaces at the sensed points. The
matrix also offers a mathematically sound way to characterize under-
constrained situations as those for which it is singular: in those
cases the kernel describes the direction of maximum uncertainty.
This paper also introduces a simple model of unstructured environments
for which the Cramer-Rao bound is a function of two statistics of
the shape of the environment: the average radius and a measure of
the irregularity of the surfaces. Although this model is not valid
for all environments, it allows for some interesting qualitative
considerations. As an experimental validation, this paper reports
simulations comparing the bound with the actual performance of the
ICP (Iterative Closest/Corresponding Point) algorithm. Finally, it
is discussed the difficulty in extending these results to find a
lower bound for accuracy in scan matching and SLAM.}
}
@inproceedings{censi06scan,
author = {Andrea Censi},
title = {Scan matching in a probabilistic framework},
booktitle = {Proceedings of the IEEE International Conference
on Robotics and Automation (ICRA)},
pages = {2291--2296},
year = {2006},
address = {Orlando, Florida},
url = {http://purl.org/censi/2006/gpm},
pdf = {http://purl.org/censi/research/2006-icra-gpm.pdf},
slides = {http://purl.org/censi/research/2006-icra-gpm-slides.pdf}
}
@inproceedings{censi05hough,
author = {Andrea Censi and Luca Iocchi and Giorgio Grisetti},
title = {Scan matching in the {H}ough domain},
booktitle = {Proceedings of the IEEE International Conference
on Robotics and Automation (ICRA)},
year = {2005},
pages = {2739--2744},
address = {Barcelona, Spain},
url = {http://purl.org/censi/2006/hsm},
pdf = {http://purl.org/censi/research/2005-icra-hsm.pdf},
slides = {http://purl.org/censi/research/2005-icra-hsm-slides.ppt}
}