18,544 research outputs found
Robust Singular Smoothers For Tracking Using Low-Fidelity Data
Tracking underwater autonomous platforms is often difficult because of noisy,
biased, and discretized input data. Classic filters and smoothers based on
standard assumptions of Gaussian white noise break down when presented with any
of these challenges. Robust models (such as the Huber loss) and constraints
(e.g. maximum velocity) are used to attenuate these issues. Here, we consider
robust smoothing with singular covariance, which covers bias and correlated
noise, as well as many specific model types, such as those used in navigation.
In particular, we show how to combine singular covariance models with robust
losses and state-space constraints in a unified framework that can handle very
low-fidelity data. A noisy, biased, and discretized navigation dataset from a
submerged, low-cost inertial measurement unit (IMU) package, with ultra short
baseline (USBL) data for ground truth, provides an opportunity to stress-test
the proposed framework with promising results. We show how robust modeling
elements improve our ability to analyze the data, and present batch processing
results for 10 minutes of data with three different frequencies of available
USBL position fixes (gaps of 30 seconds, 1 minute, and 2 minutes). The results
suggest that the framework can be extended to real-time tracking using robust
windowed estimation.Comment: 9 pages, 9 figures, to be included in Robotics: Science and Systems
201
A Box Regularized Particle Filter for state estimation with severely ambiguous and non-linear measurements
International audienceThe first stage in any control system is to be able to accurately estimate the system's state. However, some types of measurements are ambiguous (non-injective) in terms of state. Existing algorithms for such problems, such as Monte Carlo methods, are computationally expensive or not robust to such ambiguity. We propose the Box Regularized Particle Filter (BRPF) to resolve these problems. Based on previous works on box particle filters, we present a more generic and accurate formulation of the algorithm, with two innovations: a generalized box resampling step and a kernel smoothing method, which is shown to be optimal in terms of Mean Integrated Square Error. Monte Carlo simulations demonstrate the efficiency of BRPF on a severely ambiguous and non-linear estimation problem, that of Terrain Aided Navigation. BRPF is compared to the Sequential Importance Resampling Particle Filter (SIR-PF), Monte Carlo Markov Chain (MCMC), and the original Box Particle Filter (BPF). The algorithm outperforms existing methods in terms of Root Mean Square Error (e.g., improvement up to 42% in geographical position estimation with respect to the BPF) for a large initial uncertainty. The BRPF reduces the computational load by 73% and 90% for SIR-PF and MCMC, respectively, with similar RMSE values. This work offers an accurate (in terms of RMSE) and robust (in terms of divergence rate) way to tackle state estimation from ambiguous measurements while requiring a significantly lower computational load than classic Monte Carlo and particle filtering methods.The first stage in any control system is to be able to accurately estimate the system’s state. However, some types of measurements are ambiguous (non-injective) in terms of state. Existing algorithms for such problems, such as Monte Carlo methods, are computationally expensive or not robust to such ambiguity. We propose the Box Regularized Particle Filter (BRPF) to resolve these problems.Based on previous works on box particle filters, we present a more generic and accurate formulation of the algorithm, with two innovations: a generalized box resampling step and a kernel smoothing method, which is shown to be optimal in terms of Mean Integrated Square Error.Monte Carlo simulations demonstrate the efficiency of BRPF on a severely ambiguous and non-linear estimation problem, the Terrain Aided Navigation. BRPF is compared to the Sequential Importance Resampling Particle Filter (SIR-PF), the Markov Chain Monte Carlo approach (MCMC), and the original Box Particle Filter (BPF). The algorithm is demonstrated to outperform existing methods in terms of Root Mean Square Error (e.g., improvement up to 42% in geographical position estimation with respect to the BPF) for a large initial uncertainty.The BRPF yields a computational load reduction of 73% with respect to the SIR-PF and of 90% with respect to MCMC for similar RMSE orders of magnitude. The present work offers an accurate (in terms of RMSE) and robust (in terms of divergence rate) way to tackle state estimation from ambiguous measurements while requiring a significantly lower computational load than classic Monte Carlo and particle filtering methods
Robust Kalman tracking and smoothing with propagating and non-propagating outliers
A common situation in filtering where classical Kalman filtering does not
perform particularly well is tracking in the presence of propagating outliers.
This calls for robustness understood in a distributional sense, i.e.; we
enlarge the distribution assumptions made in the ideal model by suitable
neighborhoods. Based on optimality results for distributional-robust Kalman
filtering from Ruckdeschel[01,10], we propose new robust recursive filters and
smoothers designed for this purpose as well as specialized versions for
non-propagating outliers. We apply these procedures in the context of a GPS
problem arising in the car industry. To better understand these filters, we
study their behavior at stylized outlier patterns (for which they are not
designed) and compare them to other approaches for the tracking problem.
Finally, in a simulation study we discuss efficiency of our procedures in
comparison to competitors.Comment: 27 pages, 12 figures, 2 table
Robust Legged Robot State Estimation Using Factor Graph Optimization
Legged robots, specifically quadrupeds, are becoming increasingly attractive
for industrial applications such as inspection. However, to leave the
laboratory and to become useful to an end user requires reliability in harsh
conditions. From the perspective of state estimation, it is essential to be
able to accurately estimate the robot's state despite challenges such as uneven
or slippery terrain, textureless and reflective scenes, as well as dynamic
camera occlusions. We are motivated to reduce the dependency on foot contact
classifications, which fail when slipping, and to reduce position drift during
dynamic motions such as trotting. To this end, we present a factor graph
optimization method for state estimation which tightly fuses and smooths
inertial navigation, leg odometry and visual odometry. The effectiveness of the
approach is demonstrated using the ANYmal quadruped robot navigating in a
realistic outdoor industrial environment. This experiment included trotting,
walking, crossing obstacles and ascending a staircase. The proposed approach
decreased the relative position error by up to 55% and absolute position error
by 76% compared to kinematic-inertial odometry.Comment: 8 pages, 12 figures. Accepted to RA-L + IROS 2019, July 201
Multi-Lane Perception Using Feature Fusion Based on GraphSLAM
An extensive, precise and robust recognition and modeling of the environment
is a key factor for next generations of Advanced Driver Assistance Systems and
development of autonomous vehicles. In this paper, a real-time approach for the
perception of multiple lanes on highways is proposed. Lane markings detected by
camera systems and observations of other traffic participants provide the input
data for the algorithm. The information is accumulated and fused using
GraphSLAM and the result constitutes the basis for a multilane clothoid model.
To allow incorporation of additional information sources, input data is
processed in a generic format. Evaluation of the method is performed by
comparing real data, collected with an experimental vehicle on highways, to a
ground truth map. The results show that ego and adjacent lanes are robustly
detected with high quality up to a distance of 120 m. In comparison to serial
lane detection, an increase in the detection range of the ego lane and a
continuous perception of neighboring lanes is achieved. The method can
potentially be utilized for the longitudinal and lateral control of
self-driving vehicles
A particle filtering approach for joint detection/estimation of multipath effects on GPS measurements
Multipath propagation causes major impairments to Global
Positioning System (GPS) based navigation. Multipath results in biased GPS measurements, hence inaccurate position estimates. In this work, multipath effects are considered as abrupt changes affecting the navigation system. A multiple model formulation is proposed whereby the changes are represented by a discrete valued process. The detection of the errors induced by multipath is handled by a Rao-Blackwellized particle filter (RBPF). The RBPF estimates the indicator process jointly with the navigation states and multipath biases. The interest of this approach is its ability to integrate a priori constraints about the propagation environment. The detection is improved by using information from near future GPS measurements at the particle filter (PF) sampling step. A computationally modest delayed sampling is developed, which is based on a minimal duration assumption for multipath effects. Finally, the standard PF resampling stage is modified to include an hypothesis test based decision step
The path inference filter: model-based low-latency map matching of probe vehicle data
We consider the problem of reconstructing vehicle trajectories from sparse
sequences of GPS points, for which the sampling interval is between 10 seconds
and 2 minutes. We introduce a new class of algorithms, called altogether path
inference filter (PIF), that maps GPS data in real time, for a variety of
trade-offs and scenarios, and with a high throughput. Numerous prior approaches
in map-matching can be shown to be special cases of the path inference filter
presented in this article. We present an efficient procedure for automatically
training the filter on new data, with or without ground truth observations. The
framework is evaluated on a large San Francisco taxi dataset and is shown to
improve upon the current state of the art. This filter also provides insights
about driving patterns of drivers. The path inference filter has been deployed
at an industrial scale inside the Mobile Millennium traffic information system,
and is used to map fleets of data in San Francisco, Sacramento, Stockholm and
Porto.Comment: Preprint, 23 pages and 23 figure
Measuring consumption smoothing in CEX data
This paper proposes and implements a new method of measuring the degree of consumption smoothing using data from the Consumer Expenditure Survey. The structure of this Survey is such that estimators previously used in the literature are inconsistent, simply because income is measured annually and consumption is measured quarterly. We impose an AR(1) structure on the income process to obtain a proxy for quarterly income through a projection on annual income. By construction, this proxy gives rise to a measurement error which is orthogonal to the proxy itself - as opposed to the unobserved regressor - leading to a consistent estimator. We contrast our estimates with the output of two estimators used in the literature. We show that while the first (OLS) estimator tends to overstate the degree of risk sharing, the second (IV) estimator grossly understates it <br><br> Keywords; risk sharing, consumption smoothing, income risk, projection
Conditional Transformation Models
The ultimate goal of regression analysis is to obtain information about the
conditional distribution of a response given a set of explanatory variables.
This goal is, however, seldom achieved because most established regression
models only estimate the conditional mean as a function of the explanatory
variables and assume that higher moments are not affected by the regressors.
The underlying reason for such a restriction is the assumption of additivity of
signal and noise. We propose to relax this common assumption in the framework
of transformation models. The novel class of semiparametric regression models
proposed herein allows transformation functions to depend on explanatory
variables. These transformation functions are estimated by regularised
optimisation of scoring rules for probabilistic forecasts, e.g. the continuous
ranked probability score. The corresponding estimated conditional distribution
functions are consistent. Conditional transformation models are potentially
useful for describing possible heteroscedasticity, comparing spatially varying
distributions, identifying extreme events, deriving prediction intervals and
selecting variables beyond mean regression effects. An empirical investigation
based on a heteroscedastic varying coefficient simulation model demonstrates
that semiparametric estimation of conditional distribution functions can be
more beneficial than kernel-based non-parametric approaches or parametric
generalised additive models for location, scale and shape
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