Outlier Accommodation By Risk-Averse Performance-Specified Linear State Estimation

Aghapour, Elahe; Rahman, Farzana; Farrell, Jay A.

doi:10.1109/cdc.2018.8619401

Cited by 8 publications

(4 citation statements)

References 31 publications

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“…Outlier-robust estimation in control takes typically the form of secure state estimation in the presence of outliers (caused by sensor malfunctions, or measurement attacks) [65], [69], [70], [71]. In [65], [69], [70], [71], the researchers propose optimal algorithms, that achieve exact state estimation when the non-outlying measurements are noiseless. However, the algorithms have exponential run-time.…”

Section: B Outlier-robust Estimation In Statistics and Controlmentioning

confidence: 99%

Outlier-Robust Spatial Perception: Hardness, General-Purpose Algorithms, and Guarantees

Tzoumas

Antonante

Carlone

2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Spatial perception is the backbone of many robotics applications, and spans a broad range of research problems, including localization and mapping, point cloud alignment, and relative pose estimation from camera images. Robust spatial perception is jeopardized by the presence of incorrect data association, and in general, outliers. Although techniques to handle outliers do exist, they can fail in unpredictable manners (e.g., RANSAC, robust estimators), or can have exponential runtime (e.g., branch-and-bound). In this paper, we advance the state of the art in outlier rejection by making three contributions. First, we show that even a simple linear instance of outlier rejection is inapproximable: in the worst-case one cannot design a quasi-polynomial time algorithm that computes an approximate solution efficiently. Our second contribution is to provide the first per-instance sub-optimality bounds to assess the approximation quality of a given outlier rejection outcome. Our third contribution is to propose a simple general-purpose algorithm, named adaptive trimming, to remove outliers. Our algorithm leverages recently-proposed global solvers that are able to solve outlier-free problems, and iteratively removes measurements with large errors. We demonstrate the proposed algorithm on three spatial perception problems: 3D registration, two-view geometry, and SLAM. The results show that our algorithm outperforms several state-of-the-art methods across applications while being a general-purpose method. * The authors contributed equally to this work.V. Tzoumas, P. Antonante, L. Carlone are with the

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Section: B Outlier-robust Estimation In Statistics and Controlmentioning

confidence: 99%

Outlier-Robust Spatial Perception: Hardness, General-Purpose Algorithms, and Guarantees

Tzoumas

Antonante

Carlone

2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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“…Some authors performed wide practical analysis of signal-in-space ranging errors for all current satellite navigation systems [12][13][14][15][16][17][18]. The satellites orbit errors and clock biases are the keys to precise point positioning [19][20][21][22][23][24][25][26]. So increasing the positioning precision is the primary goal of GNSS users.…”

Section: Introductionmentioning

confidence: 99%

Metrology and Measurement Systems

Skeivalas¹,

Paršeliūnas²,

Putrimas³

2020

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The possibilities to improve values of the satellite orbit elements by employing the pseudo-ranges and differences of carrier phase frequencies measured at many reference GPS stations are analysed. An improvement of orbit ephemeris is achieved by solving an equation system of corrections of the pseudo-ranges and phase differences with the least-squares method. Also, equations of space coordinates of satellite orbit points expressed by ephemeris at fixed moments are used. The relation between the accuracy of the pseudo-ranges and phase differences and the accuracy of the satellite ephemeris is analysed. Formulae for estimation of the influence of the ephemeris on the measured pseudo-ranges and phase differences and for prediction of the accuracy of the pseudo-ranges and phase differences were obtained. An influence of the covariance between single orbit parameters on the accuracy of the pseudo-ranges and phase differences is detected.

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“…In the work of Carlone et al, a maximum subset of pose graph measurements is selected that is consistent with the same system. The risk‐averse performance‐specified (RAPS) approach is also optimization based, selecting the subset of measurements that yields the minimum risk state estimate that satisfies an accuracy specification. The approach uses a measurement selection vector .…”

Section: Introductionmentioning

confidence: 99%

Outlier accommodation in moving‐horizon state estimation: A risk‐averse performance‐specified approach

Aghapour

Farrell

2019

Adaptive Control & Signal

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Summary This paper presents a novel state estimation approach for linear dynamic systems when measurements are corrupted by outliers. Since outliers can degrade the performance of state estimation, outlier accommodation is critical. The standard approach combines outlier detection utilizing Neyman‐Pearson (NP) type tests with a Kalman filter (KF). This approach ignores all residuals greater than a designer‐specified threshold. When measurements with outliers are used (ie, missed detections), both the state estimate and the error covariance matrix become corrupted. This corrupted state and covariance estimate are then the basis for all subsequent outlier decisions. When valid measurements are rejected (ie, false alarms), potentially using the corrupted state estimate and error covariance, measurement information is lost. Either using invalid information or discarding too much valid information can result in divergence of the KF. An alternative approach is moving‐horizon (MH) state estimation, which maintains all recent measurement data within a moving window with a time horizon of length L. In MH approaches, the number of measurements available for state estimation is affected by both the number of measurements per time step and the number of time steps L over which measurements are retained. Risk‐averse performance‐specified (RAPS) state estimation works within an optimization setting to choose a set of measurements that achieves a performance specification with minimum risk of outlier inclusion. This paper derives and formulates the MH‐RAPS solution for outlier accommodation. The paper also presents implementation results. The MH‐RAPS application uses Global Navigation Satellite Systems measurements to estimate the state of a moving platform using a position, velocity, and acceleration model. In this application, MH‐RAPS performance is compared with MH‐NP state estimation.

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Outlier Accommodation By Risk-Averse Performance-Specified Linear State Estimation

Cited by 8 publications

References 31 publications

Outlier-Robust Spatial Perception: Hardness, General-Purpose Algorithms, and Guarantees

Outlier-Robust Spatial Perception: Hardness, General-Purpose Algorithms, and Guarantees

Metrology and Measurement Systems

Outlier accommodation in moving‐horizon state estimation: A risk‐averse performance‐specified approach

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