Multiple GPS Fault Detection and Isolation Using a Graph-SLAM Framework

Bhamidipati, Sriramya; Gao, Grace Xingxin

doi:10.33012/2018.16030

Cited by 9 publications

(19 citation statements)

References 13 publications

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“…Moreover, FGO handles delayed measurements, as these are simply additional sources of factors that are added to the factor graph as they are received. Thus, FGO is used in various challenging GNSS scenarios (Bhamidipati, 2018; Huang, 2016; Watson & Gross, 2018; Zheng Gong, 2018), and the authors in (Pfeifer & Protzel, 2018) showed the strong potential of FGO in sensor fusion, even when the sensor noise is modeled with a non‐Gaussian distribution.…”

Section: Related Workmentioning

confidence: 99%

Factor graph optimization for GNSS/INS integration: A comparison with the extended Kalman filter

et al. 2021

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Factor graph optimization (FGO) recently has attracted attention as an alternative to the extended Kalman filter (EKF) for GNSS‐INS integration. This study evaluates both loosely and tightly coupled integrations of GNSS code pseudorange and INS measurements for real‐time positioning, using both conventional EKF and FGO with a dataset collected in an urban canyon in Hong Kong. The FGO strength is analyzed by degenerating the FGO‐based estimator into an “EKF‐like estimator.” In addition, the effects of window size on FGO performance are evaluated by considering both the GNSS pseudorange error models and environmental conditions. We conclude that the conventional FGO outperforms the EKF because of the following two factors: (1) FGO uses multiple iterations during the estimation to achieve a robust estimation; and (2) FGO better explores the time correlation between the measurements and states, based on a batch of historical data, when the measurements do not follow the Gaussian noise assumption.

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Section: Related Workmentioning

confidence: 99%

Factor graph optimization for GNSS/INS integration: A comparison with the extended Kalman filter

et al. 2021

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“…-Σ t andΩ k t denotes the predicted covariance matrix of the vehicle state vector and k th satellite state vector at the t th time instant; Explanation regarding estimating these covariances is given in our prior work [11]; -σ k t denote the measurement covariance of the k th satellite and is estimated from Eq. (6); Similarly, ω t (u) denotes the covariance associated with the intensity of the non-sky pixel u and is estimated based on Section 2.3 of [28].…”

Section: Extended Graph Optimizationmentioning

confidence: 99%

“…Using the linearized equations seen in Eqs. (11), (12) and (13), we derive the failure slope for the graph optimization framework in terms of unknown fault vector. For this, we consider CKC ≈ I, which is valid approximation after the iterative convergence of the graph optimization at any time instant since β << 0.…”

Section: Gps Faultsmentioning

confidence: 99%

“…t S −1 t , V t A T t R −1 t , V t J T t P −1 t ],(12)where -K t denotes the estimation matrix of the graph-optimization framework and V denotes the pseudo-inverse matrix, suchthat V t = H T t S −1 t H t + A T t R −1 t A t + J T t P −1 t J t + β diag(H T t H t + A T t A t + J T t J t ) −1 ;-S t , R t , P t denotes the M-estimator-based weight functions for the GPS pseudoranges, vehicle motion model and satellite orbital model, non-sky image pixel intensities, respectively, and evaluated at θ =θ t ; Details regarding the choice of Mestimator and the corresponding weight functions are explained in our prior work[11];β n,t denotes the iterative damping factor associated with the Levenberg Marquardt algorithm.…”

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confidence: 99%

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SLAM-based Integrity Monitoring Using GPS and Fish-eye Camera

Bhamidipati¹,

Gao²

2019

Proceedings of the 32nd International Technical Meeting of the Satellite Division of the Institute of Navigation (ION GNSS+ 201

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Urban navigation using GPS and fish-eye camera suffers from multipath effects in GPS measurements and data association errors in pixel intensities across image frames. We propose a Simultaneous Localization and Mapping (SLAM)-based Integrity Monitoring (IM) algorithm to compute the position protection levels while accounting for multiple faults in both GPS and vision. We perform graph optimization using the sequential data of GPS pseudoranges, pixel intensities, vehicle dynamics and satellite ephemeris to simultaneously localize the vehicle as well as the landmarks, namely GPS satellites and key image pixels in the world frame. We estimate the fault mode vector by analyzing the temporal correlation across the GPS measurement residuals and spatial correlation across the vision intensity residuals. In particular, to detect and isolate the vision faults, we developed a superpixel-based piecewise Random Sample Consensus (RANSAC) technique to perform spatial voting across image pixels. For an estimated fault mode, we compute the protection levels by applying worst-case failure slope analysis to the linearized Graph-SLAM framework.We perform ground vehicle experiments in the semi-urban area of Champaign, IL and have demonstrated the successful detection and isolation of multiple faults. We also validate tighter protection levels and lower localization errors achieved via the proposed algorithm as compared to SLAM-based IM that utilizes only GPS measurements.

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“…While research on GPS-vision navigation [7][8][9] is gaining momentum, to the best of our knowledge, there is only limited literature on the integrity of sensor fusion. In our prior works [10][11][12], we developed a GPS-vision Simultaneous Localization And Mapping (SLAM)-based Integrity Monitoring (IM) that performs multiple Fault Detection and Exclusion (FDE) via analysis of temporal correlation across GPS measurement residuals, and the spatial correlation across vision intensity residuals. The authors of [13] proposed an IM framework wherein they utilized a fish-eye camera for isolating GPS faults with an assumption that the faults in vision measurements are negligible.…”

Section: Introductionmentioning

confidence: 99%