Adaptive Cubature Split Covariance Intersection Filter for Multi-Vehicle Cooperative Localization

Fang, Susu; Li, Hao; Yang, Ming

doi:10.1109/lra.2021.3137889

Cited by 9 publications

(2 citation statements)

References 36 publications

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“…In future work, we would like to research the cubature split covariance intersection filter (CSCIF) [43] and adaptive cubature split covariance intersection filter (ACSCIF) [44]. Data Availability Statement: Some or all data, models, or codes that support the findings of this study are available from the corresponding author upon reasonable request.…”

Section: Discussionmentioning

confidence: 99%

Cooperative Vehicle Localization in Multi-Sensor Multi-Vehicle Systems Based on an Interval Split Covariance Intersection Filter with Fault Detection and Exclusion

Shan,

Cabani,

Chafouk

2024

Vehicles

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In the cooperative multi-sensor multi-vehicle (MSMV) localization domain, the data incest problem yields inconsistent data fusion results, thereby reducing the accuracy of vehicle localization. In order to address this problem, we propose the interval split covariance intersection filter (ISCIF). At first, the proposed ISCIF method is applied to the absolute positioning step. Then, we combine the interval constraint propagation (ICP) method and the proposed ISCIF method to realize relative positioning. Additionally, in order to enhance the robustness of the MSMV localization system, a Kullback–Leibler divergence (KLD)-based fault detection and exclusion (FDE) method is implemented in our system. Three simulations were carried out: Simulation scenarios 1 and 2 aimed to assess the accuracy of the proposed ISCIF with various capabilities of absolute vehicle positioning, while simulation scenario 3 was designed to evaluate the localization performance when faults were present. The simulation results of scenarios 1 and 2 demonstrated that our proposed vehicle localization method reduced the root mean square error (RMSE) by 8.9% and 15.5%, respectively, compared to the conventional split covariance intersection filter (SCIF) method. The simulation results of scenario 3 indicated that the implemented FDE method could effectively reduce the RMSE of vehicles (by about 55%) when faults were present in the system.

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Section: Discussionmentioning

confidence: 99%

Cooperative Vehicle Localization in Multi-Sensor Multi-Vehicle Systems Based on an Interval Split Covariance Intersection Filter with Fault Detection and Exclusion

Shan,

Cabani,

Chafouk

2024

Vehicles

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“…In Xu, Asghar & Liu (2019) , a Huber-based robust algorithm is proposed for cooperative localization of autonomous underwater vehicles, which adopts an adaptive noise estimation method to estimate the non-Gaussian noise. In Fang, Li & Yang (2021) , an adaptive cubature split covariance intersection filter is used to estimate the vehicle state, the advantage of which is that it uses the cubature transform and innovation-based adaptive estimation theory to deal with dynamic measurement noise. However, these two works did not consider the scenes with abnormal measurements.…”

Section: Introductionmentioning

confidence: 99%

A robust cooperative localization algorithm based on covariance intersection method for multi-robot systems

Wang

Liu

2023

PeerJ Computer Science

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Cooperative localization is an arising research problem for multi-robot system, especially for the scenarios that need to reduce the communication load of base stations. This article proposes a novel cooperative localization algorithm, which can achieve high accuracy localization by using the relative measurements among robots. To address uncertainty in the measuring robots’ positions and avoid linearization errors in the extended Kalman filter during the measurement update phase, a particle-based approximation method is proposed. The covariance intersection method is then employed to fuse preliminary estimations from different robots, guaranteeing a minimum upper bound for the fused covariance. Moreover, in order to avoid the negative effect of abnormal measurements, this article adopts the Kullback–Leibler divergence to calculate the distances between different estimations and rejects to fuse the preliminary estimations far from the estimation obtained in the prediction stage. Two simulations are conducted to validate the proposed algorithm. Compared with the other three algorithms, the proposed algorithm can achieve higher localization accuracy and deal with the abnormal measurement.

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Prospective study on challenges faced in a perception system

L. R.,

Bhat,

K. V.

2024

Cogent Engineering

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Adaptive Cubature Split Covariance Intersection Filter for Multi-Vehicle Cooperative Localization

Cited by 9 publications

References 36 publications

Cooperative Vehicle Localization in Multi-Sensor Multi-Vehicle Systems Based on an Interval Split Covariance Intersection Filter with Fault Detection and Exclusion

Cooperative Vehicle Localization in Multi-Sensor Multi-Vehicle Systems Based on an Interval Split Covariance Intersection Filter with Fault Detection and Exclusion

A robust cooperative localization algorithm based on covariance intersection method for multi-robot systems

Prospective study on challenges faced in a perception system

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