20,581 research outputs found
Benchmarking Particle Filter Algorithms for Efficient Velodyne-Based Vehicle Localization
Keeping a vehicle well-localized within a prebuilt-map is at the core of any autonomous vehicle navigation system. In this work, we show that both standard SIR sampling and rejection-based optimal sampling are suitable for efficient (10 to 20 ms) real-time pose tracking without feature detection that is using raw point clouds from a 3D LiDAR. Motivated by the large amount of information captured by these sensors, we perform a systematic statistical analysis of how many points are actually required to reach an optimal ratio between efficiency and positioning accuracy. Furthermore, initialization from adverse conditions, e.g., poor GPS signal in urban canyons, we also identify the optimal particle filter settings required to ensure convergence. Our findings include that a decimation factor between 100 and 200 on incoming point clouds provides a large savings in computational cost with a negligible loss in localization accuracy for a VLP-16 scanner. Furthermore, an initial density of ∼2 particles/m 2 is required to achieve 100% convergence success for large-scale (∼100,000 m 2 ), outdoor global localization without any additional hint from GPS or magnetic field sensors. All implementations have been released as open-source software
Modeling and interpolation of the ambient magnetic field by Gaussian processes
Anomalies in the ambient magnetic field can be used as features in indoor
positioning and navigation. By using Maxwell's equations, we derive and present
a Bayesian non-parametric probabilistic modeling approach for interpolation and
extrapolation of the magnetic field. We model the magnetic field components
jointly by imposing a Gaussian process (GP) prior on the latent scalar
potential of the magnetic field. By rewriting the GP model in terms of a
Hilbert space representation, we circumvent the computational pitfalls
associated with GP modeling and provide a computationally efficient and
physically justified modeling tool for the ambient magnetic field. The model
allows for sequential updating of the estimate and time-dependent changes in
the magnetic field. The model is shown to work well in practice in different
applications: we demonstrate mapping of the magnetic field both with an
inexpensive Raspberry Pi powered robot and on foot using a standard smartphone.Comment: 17 pages, 12 figures, to appear in IEEE Transactions on Robotic
Where Does the Density Localize? Convergent Behavior for Global Hybrids, Range Separation, and DFT+U
Approximate density functional theory (DFT) suffers from many-electron self-
interaction error, otherwise known as delocalization error, that may be
diagnosed and then corrected through elimination of the deviation from exact
piecewise linear behavior between integer electron numbers. Although paths to
correction of energetic delocalization error are well- established, the impact
of these corrections on the electron density is less well-studied. Here, we
compare the effect on density delocalization of DFT+U, global hybrid tuning,
and range- separated hybrid tuning on a diverse test set of 32 transition metal
complexes and observe the three methods to have qualitatively equivalent
effects on the ground state density. Regardless of valence orbital diffuseness
(i.e., from 2p to 5p), ligand electronegativity (i.e., from Al to O), basis set
(i.e., plane wave versus localized basis set), metal (i.e., Ti, Fe, Ni) and
spin state, or tuning method, we consistently observe substantial charge loss
at the metal and gain at ligand atoms (ca. 0.3-0.5 e or more). This charge loss
at the metal is preferentially from the minority spin, leading to increasing
magnetic moment as well. Using accurate wavefunction theory references, we
observe that a minimum error in partial charges and magnetic moments occur at
higher tuning parameters than typically employed to eliminate energetic
delocalization error. These observations motivate the need to develop
multi-faceted approximate-DFT error correction approaches that separately treat
density delocalization and energetic errors in order to recover both correct
density and magnetization properties.Comment: 34 pages, 11 figure
CoBe -- Coded Beacons for Localization, Object Tracking, and SLAM Augmentation
This paper presents a novel beacon light coding protocol, which enables fast
and accurate identification of the beacons in an image. The protocol is
provably robust to a predefined set of detection and decoding errors, and does
not require any synchronization between the beacons themselves and the optical
sensor. A detailed guide is then given for developing an optical tracking and
localization system, which is based on the suggested protocol and readily
available hardware. Such a system operates either as a standalone system for
recovering the six degrees of freedom of fast moving objects, or integrated
with existing SLAM pipelines providing them with error-free and easily
identifiable landmarks. Based on this guide, we implemented a low-cost
positional tracking system which can run in real-time on an IoT board. We
evaluate our system's accuracy and compare it to other popular methods which
utilize the same optical hardware, in experiments where the ground truth is
known. A companion video containing multiple real-world experiments
demonstrates the accuracy, speed, and applicability of the proposed system in a
wide range of environments and real-world tasks. Open source code is provided
to encourage further development of low-cost localization systems integrating
the suggested technology at its navigation core
Structural dynamics branch research and accomplishments to FY 1992
This publication contains a collection of fiscal year 1992 research highlights from the Structural Dynamics Branch at NASA LeRC. Highlights from the branch's major work areas--Aeroelasticity, Vibration Control, Dynamic Systems, and Computational Structural Methods are included in the report as well as a listing of the fiscal year 1992 branch publications
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