An adaptive nonparametric particle filter for state estimation

Wang, Yali; Chaib-draa, Brahim

doi:10.1109/icra.2012.6224840

Cited by 6 publications

(3 citation statements)

References 15 publications

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“…Generalized filtering techniques (such as Kalman filters, and Particle filters) have long been applied to robot arms which have only partially observable state. For instance, recent works have tracked the state of flexible robot arms using inertial sensors on the end effector [1] and from motor voltage inputs alone using a learned dynamic model and particle filter [29].…”

Section: Related Workmentioning

confidence: 99%

Articulated Robot Motion for Simultaneous Localization and Mapping (ARM-SLAM)

Klingensmith

Sirinivasa

Kaess

2016

IEEE Robot. Autom. Lett.

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A robot with a hand-mounted depth sensor scans a scene. When the robot’s joint angles are not known with certainty, how can it best reconstruct the scene? In this work, we simultaneously estimate the joint angles of the robot and reconstruct a dense volumetric model of the scene. In this way, we perform simultaneous localization and mapping in the configuration space of the robot, rather than in the pose space of the camera. We show using simulations and robot experiments that our approach greatly reduces both 3D reconstruction error and joint angle error over simply using the forward kinematics. Unlike other approaches, ours directly reasons about robot joint angles, and can use these to constrain the pose of the sensor. Because of this, it is more robust to missing or ambiguous depth data than approaches that are unconstrained by the robot’s kinematics.

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Section: Related Workmentioning

confidence: 99%

Articulated Robot Motion for Simultaneous Localization and Mapping (ARM-SLAM)

Klingensmith

Sirinivasa

Kaess

2016

IEEE Robot. Autom. Lett.

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“…However, the challenge in the selection of proposal distribution is to find an appropriate covariance matrix for the random walk. In general, it is difficult to design such a proposal since it produces the variance of likelihood distribution, which is usually relatively small, and this leads to the inaccurate approximation of the target distribution [ 36 ]. Moreover, in the majority of cases, nonlinearity or non-Gaussianity makes an analytic solution intractable [ 37 ].…”

Section: Non-gaussian Delayed Particle Smoothing Methods and Computmentioning

confidence: 99%

A Nonlinear Framework of Delayed Particle Smoothing Method for Vehicle Localization under Non-Gaussian Environment

Xiao

Havyarimana

2016

Sensors

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In this paper, a novel nonlinear framework of smoothing method, non-Gaussian delayed particle smoother (nGDPS), is proposed, which enables vehicle state estimation (VSE) with high accuracy taking into account the non-Gaussianity of the measurement and process noises. Within the proposed method, the multivariate Student’s t-distribution is adopted in order to compute the probability distribution function (PDF) related to the process and measurement noises, which are assumed to be non-Gaussian distributed. A computation approach based on Ensemble Kalman Filter (EnKF) is designed to cope with the mean and the covariance matrix of the proposal non-Gaussian distribution. A delayed Gibbs sampling algorithm, which incorporates smoothing of the sampled trajectories over a fixed-delay, is proposed to deal with the sample degeneracy of particles. The performance is investigated based on the real-world data, which is collected by low-cost on-board vehicle sensors. The comparison study based on the real-world experiments and the statistical analysis demonstrates that the proposed nGDPS has significant improvement on the vehicle state accuracy and outperforms the existing filtering and smoothing methods.

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“…One way of reducing the degeneracy phenomenon is to use very large sample size. However, a very large sample size with a xed number of particles results in approximation with low computational eciency, especially when the true posterior changes over time [Wang & Chaib-draa 2012]. Several methods have been introduced to prevent the degeneracy problem [Arnaud Doucet 2001], [Robert & Casella 2005].…”

Section: 8mentioning

confidence: 99%

Nonlinear Attitude and Formation Estimation Spacecraft And Multi-Agent Systems

Rososhansky¹

2021

Preprint

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This dissertation examines the state and parameter estimation problem of monolithic spacecraft and multi-agent systems in conjunction with the control algorithms. Nonlinear filtering techniques are investigated and applied to the problems of attitude estimation and control of monolithic spacecraft, distributed flltering for attitude estimation and control of satellite formation flying (SFF), and estimation and control of a multi-agent system in consensus tracking with uncertain dynamic model. The main objective is to investigate the performance of nonlinear filtering techniques under fault-free and fault-prone scenarios. In essence, the core of this research has been placed on identifying techniques to improve the efficiency and reduce the variance of estimations in nonlinear filtering. The research is primarily dedicated to the investigation of adaptive unscented Kalman Filter (AUKF) and particle Filter (PF). A nonlinear filtering technique has been proposed for sequential joint estimation of a multi-agent system in consensus tracking with uncertain dynamic model. The new filter is called marginalized unscented particle Filter (MUPF). The proposed filter uses the Rao-Blackwellised principle to couple the particle filtering technique with unscented transform algorithm

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An adaptive nonparametric particle filter for state estimation

Cited by 6 publications

References 15 publications

Articulated Robot Motion for Simultaneous Localization and Mapping (ARM-SLAM)

Articulated Robot Motion for Simultaneous Localization and Mapping (ARM-SLAM)

A Nonlinear Framework of Delayed Particle Smoothing Method for Vehicle Localization under Non-Gaussian Environment

Nonlinear Attitude and Formation Estimation Spacecraft And Multi-Agent Systems

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