42,605 research outputs found
The Coordinate Particle Filter - A novel Particle Filter for High Dimensional Systems
Parametric filters, such as the Extended Kalman Filter and the Unscented
Kalman Filter, typically scale well with the dimensionality of the problem, but
they are known to fail if the posterior state distribution cannot be closely
approximated by a density of the assumed parametric form. For nonparametric
filters, such as the Particle Filter, the converse holds. Such methods are able
to approximate any posterior, but the computational requirements scale
exponentially with the number of dimensions of the state space. In this paper,
we present the Coordinate Particle Filter which alleviates this problem. We
propose to compute the particle weights recursively, dimension by dimension.
This allows us to explore one dimension at a time, and resample after each
dimension if necessary. Experimental results on simulated as well as real data
confirm that the proposed method has a substantial performance advantage over
the Particle Filter in high-dimensional systems where not all dimensions are
highly correlated. We demonstrate the benefits of the proposed method for the
problem of multi-object and robotic manipulator tracking
A Novel Generic Framework for Track Fitting in Complex Detector Systems
This paper presents a novel framework for track fitting which is usable in a
wide range of experiments, independent of the specific event topology, detector
setup, or magnetic field arrangement. This goal is achieved through a
completely modular design. Fitting algorithms are implemented as
interchangeable modules. At present, the framework contains a validated Kalman
filter. Track parameterizations and the routines required to extrapolate the
track parameters and their covariance matrices through the experiment are also
implemented as interchangeable modules. Different track parameterizations and
extrapolation routines can be used simultaneously for fitting of the same
physical track. Representations of detector hits are the third modular
ingredient to the framework. The hit dimensionality and orientation of planar
tracking detectors are not restricted. Tracking information from detectors
which do not measure the passage of particles in a fixed physical detector
plane, e.g. drift chambers or TPCs, is used without any simplifications. The
concept is implemented in a light-weight C++ library called GENFIT, which is
available as free software
CDDT: Fast Approximate 2D Ray Casting for Accelerated Localization
Localization is an essential component for autonomous robots. A
well-established localization approach combines ray casting with a particle
filter, leading to a computationally expensive algorithm that is difficult to
run on resource-constrained mobile robots. We present a novel data structure
called the Compressed Directional Distance Transform for accelerating ray
casting in two dimensional occupancy grid maps. Our approach allows online map
updates, and near constant time ray casting performance for a fixed size map,
in contrast with other methods which exhibit poor worst case performance. Our
experimental results show that the proposed algorithm approximates the
performance characteristics of reading from a three dimensional lookup table of
ray cast solutions while requiring two orders of magnitude less memory and
precomputation. This results in a particle filter algorithm which can maintain
2500 particles with 61 ray casts per particle at 40Hz, using a single CPU
thread onboard a mobile robot.Comment: 8 pages, 14 figures, ICRA versio
Vehicle detection and tracking using homography-based plane rectification and particle filtering
This paper presents a full system for vehicle detection and tracking in non-stationary settings based on computer vision. The method proposed for vehicle detection exploits the geometrical relations between the elements in the scene so that moving objects (i.e., vehicles) can be detected by analyzing motion parallax. Namely, the homography of the road plane between successive images is computed. Most remarkably, a novel probabilistic framework based on Kalman filtering is presented for reliable and accurate homography estimation. The estimated homography is used for image alignment, which in turn allows to detect the moving vehicles in the image. Tracking of vehicles is performed on the basis of a multidimensional particle filter, which also manages the exit and entries of objects. The filter involves a mixture likelihood model that allows a better adaptation of the particles to the observed measurements. The system is specially designed for highway environments, where it has been proven to yield excellent results
Particle Detection Algorithms for Complex Plasmas
In complex plasmas, the behavior of freely floating micrometer sized
particles is studied. The particles can be directly visualized and recorded by
digital video cameras. To analyze the dynamics of single particles, reliable
algorithms are required to accurately determine their positions to sub-pixel
accuracy from the recorded images. Typically, straightforward algorithms are
used for this task. Here, we combine the algorithms with common techniques for
image processing. We study several algorithms and pre- and post-processing
methods, and we investigate the impact of the choice of threshold parameters,
including an automatic threshold detection. The results quantitatively show
that each algorithm and method has its own advantage, often depending on the
problem at hand. This knowledge is applicable not only to complex plasmas, but
useful for any kind of comparable image-based particle tracking, e.g. in the
field of colloids or granular matter
3D angle-of-arrival positioning using von Mises-Fisher distribution
We propose modeling an angle-of-arrival (AOA) positioning measurement as a
von Mises-Fisher (VMF) distributed unit vector instead of the conventional
normally distributed azimuth and elevation measurements. Describing the
2-dimensional AOA measurement with three numbers removes discontinuities and
reduces nonlinearity at the poles of the azimuth-elevation coordinate system.
Our computer simulations show that the proposed VMF measurement noise model
based filters outperform the normal distribution based algorithms in accuracy
in a scenario where close-to-pole measurements occur frequently.Comment: 5 page
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