13,113 research outputs found
Extended Object Tracking: Introduction, Overview and Applications
This article provides an elaborate overview of current research in extended
object tracking. We provide a clear definition of the extended object tracking
problem and discuss its delimitation to other types of object tracking. Next,
different aspects of extended object modelling are extensively discussed.
Subsequently, we give a tutorial introduction to two basic and well used
extended object tracking approaches - the random matrix approach and the Kalman
filter-based approach for star-convex shapes. The next part treats the tracking
of multiple extended objects and elaborates how the large number of feasible
association hypotheses can be tackled using both Random Finite Set (RFS) and
Non-RFS multi-object trackers. The article concludes with a summary of current
applications, where four example applications involving camera, X-band radar,
light detection and ranging (lidar), red-green-blue-depth (RGB-D) sensors are
highlighted.Comment: 30 pages, 19 figure
Learning Task Priorities from Demonstrations
Bimanual operations in humanoids offer the possibility to carry out more than
one manipulation task at the same time, which in turn introduces the problem of
task prioritization. We address this problem from a learning from demonstration
perspective, by extending the Task-Parameterized Gaussian Mixture Model
(TP-GMM) to Jacobian and null space structures. The proposed approach is tested
on bimanual skills but can be applied in any scenario where the prioritization
between potentially conflicting tasks needs to be learned. We evaluate the
proposed framework in: two different tasks with humanoids requiring the
learning of priorities and a loco-manipulation scenario, showing that the
approach can be exploited to learn the prioritization of multiple tasks in
parallel.Comment: Accepted for publication at the IEEE Transactions on Robotic
Compensation of Magnetic Disturbances Improves Inertial and Magnetic Sensing of Human Body Segment Orientation
This paper describes a complementary Kalman filter design to estimate orientation of human body segments by fusing gyroscope, accelerometer, and magnetometer signals from miniature sensors. Ferromagnetic materials or other magnetic fields near the sensor module disturb the local earth magnetic field and, therefore, the orientation estimation, which impedes many (ambulatory) applications. In the filter, the gyroscope bias error, orientation error, and magnetic disturbance error are estimated. The filter was tested under quasi-static and dynamic conditions with ferromagnetic materials close to the sensor module. The quasi-static experiments implied static positions and rotations around the three axes. In the dynamic experiments, three-dimensional rotations were performed near a metal tool case. The orientation estimated by the filter was compared with the orientation obtained with an optical reference system Vicon. Results show accurate and drift-free orientation estimates. The compensation results in a significant difference (p<0.01) between the orientation estimates with compensation of magnetic disturbances in comparison to no compensation or only gyroscopes. The average static error was 1.4/spl deg/ (standard deviation 0.4) in the magnetically disturbed experiments. The dynamic error was 2.6/spl deg/ root means square
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
Recursive Estimation of Orientation Based on the Bingham Distribution
Directional estimation is a common problem in many tracking applications.
Traditional filters such as the Kalman filter perform poorly because they fail
to take the periodic nature of the problem into account. We present a recursive
filter for directional data based on the Bingham distribution in two
dimensions. The proposed filter can be applied to circular filtering problems
with 180 degree symmetry, i.e., rotations by 180 degrees cannot be
distinguished. It is easily implemented using standard numerical techniques and
suitable for real-time applications. The presented approach is extensible to
quaternions, which allow tracking arbitrary three-dimensional orientations. We
evaluate our filter in a challenging scenario and compare it to a traditional
Kalman filtering approach
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