Outlier-insensitive Kalman smoothing and marginal message passing

Wadehn, Federico; Bruderer, Lukas; Dauwels, Justin; Sahdeva, Vijay; Yu, Hao; Loeliger, Hans-Andrea

doi:10.1109/eusipco.2016.7760447

Cited by 14 publications

(11 citation statements)

References 16 publications

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“…This is because KF methods are generally sensitive to outliers and there is a need for a more robust outlier detection method to handle tracking inaccuracies. Wadehn et al show in [23], a smoother that was put in place to address possible outliers. Others, such as Ting et al in [22] followed a weighted least squares approach by applying weights to data observations to tackle outliers and hence improve tracking robustness.…”

Section: Proposed Methodsmentioning

confidence: 99%

Over Time RF Fitting for Jitter Free 3D Vertebra Reconstruction from Video Fluoroscopy

Ioannidis

Nait-Charif

2019

Computer Analysis of Images and Patterns

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Over the past decades, there has been an increasing interest in spine kinematics and various approaches have been proposed on how to analyse spine kinematics. Amongst all, emphasis has been given to both the shape of the individual vertebrae as well as the overall spine curvature as a mean of providing accurate and valid spinal condition diagnosis. Traditional invasive methods cannot accurately delineate the intersegmental motion of the spine vertebrae. On the contrary, capturing and measuring spinal motion via the non-invasive fluoroscopy has been a popular technique choice because of its low incurred patient radiation exposure nature. In general, image-based 3D reconstruction methods focus on static spine instances. However, even the ones analysing sequences yield in unstable and jittery animations of the reconstructed spine. In this paper, we address this issue using a novel approach to robustly reconstruct and rigidly derive a shape with no inter-frame variations. This is to produce animations that are jitter free across our sequence based on fluoroscopy video. Our main contributions are 1) retaining the shape of the solid vertebrae across the frame range, 2) helping towards a more accurate image segmentation even when there's a limited training set. We show our pipeline's success by reconstructing and comparing 3D animations of the lumbar spine from a corresponding fluoroscopic video.

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Section: Proposed Methodsmentioning

confidence: 99%

Over Time RF Fitting for Jitter Free 3D Vertebra Reconstruction from Video Fluoroscopy

Ioannidis

Nait-Charif

2019

Computer Analysis of Images and Patterns

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“…To exploit all the information that is available during the calibration stage, a smoothing step is applied after the filter and performed backward on the whole trajectory. The literature contains many smoothing methods [15], [1], [9]. In this paper we use the Rauch-Tung-Striebel smoother [13] (also known as Kalman smoother), since it is the simplest method and directly extends Kalman filtering.…”

Section: B Smoothingmentioning

confidence: 99%

Four-Wheeled Dead-Reckoning Model Calibration using RTS Smoothing

Welte

2019

2019 International Conference on Robotics and Automation (ICRA)

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Localization is one of the main challenges to be addressed to develop autonomous vehicles able to perform complex maneuvers on roads opened to public traffic. Having an accurate dead-reckoning system is an essential step to reach this objective. This paper presents a dead-reckoning model for carlike vehicles that performs the data fusion of complementary and redundant sensors: wheel encoders, yaw rate gyro and steering wheel measurements. In order to get an accurate deadreckoning system with a drift reduced to the minimum, the parameters have to be well calibrated and the procedure has to be simple and efficient. We present a method able to accurately calibrate the parameters without knowing the ground truth by using a Rauch-Tung-Striebel smoothing scheme which enables to obtain state estimates as close to the ground truth as possible. The smoothed estimates are then used within a optimization process to calibrate the model parameters. The method has been tested using data recorded from an experimental vehicle on public roads. The results show a significant diminution of the dead-reckoning drift compared to a commonly used calibration method. We evaluate finally the average distance a vehicle can navigate without exteroceptive sensors by using the proposed four-wheeled dead reckoning system.

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“…It is worth mentioning that the application of FG methods to Bayesian smoothing is not new. However, as far as we know, the few results available in the technical literature about this topic refer to the case of linear Gaussian SSMs only [23], [25], [26], whereas we exclusively focus on the case in which the mathematical laws expressing state dynamics and/or available observations are nonlinear.…”

Section: Introductionmentioning

confidence: 99%

Particle Smoothing for Conditionally Linear Gaussian Models as Message Passing Over Factor Graphs

Vitetta

Sirignano

Montorsi³

2018

IEEE Trans. Signal Process.

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In this manuscript the fixed-lag smoothing problem for conditionally linear Gaussian state-space models is investigated from a factor graph perspective. More specifically, after formulating Bayesian smoothing for an arbitrary state-space model as forward-backward message passing over a factor graph, we focus on the above mentioned class of models and derive two novel particle smoothers for it. Both the proposed techniques are based on the well known two-filter smoothing approach and employ marginalized particle filtering in their forward pass. However, on the one hand, the first smoothing technique can only be employed to improve the accuracy of state estimates with respect to that achieved by forward filtering. On the other hand, the second method, that belongs to the class of Rao-Blackwellized particle smoothers, provides also a point mass approximation of the so called joint smoothing distribution. Finally, our smoothing algorithms are compared, in terms of estimation accuracy and computational requirements, with a Rao-Blackwellized particle smoother recently proposed by Lindsten et al. in [20].

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Outlier-insensitive Kalman smoothing and marginal message passing

Cited by 14 publications

References 16 publications

Over Time RF Fitting for Jitter Free 3D Vertebra Reconstruction from Video Fluoroscopy

Over Time RF Fitting for Jitter Free 3D Vertebra Reconstruction from Video Fluoroscopy

Four-Wheeled Dead-Reckoning Model Calibration using RTS Smoothing

Particle Smoothing for Conditionally Linear Gaussian Models as Message Passing Over Factor Graphs

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