Ensemble transform particle filter using regularized optimal transport and measure of nonlinearity

Kang, Chang Ho; Kim, Sun Young; Choe, Yeongkwon; Park, Chan Gook

doi:10.1016/j.measurement.2019.06.046

Cited by 4 publications

(3 citation statements)

References 21 publications

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“…For improving the integrated transform Particle filter, Kang and other scholars replaced the linear transformation step with an algorithm in view of the regularized optimal transmission, and then proposed to use the improved Sinkhorn Knopp algorithm to reduce the computational cost through the convergence of the process iteration rate. The final results indicate that this method has good practicality 20 …”

Section: Related Workmentioning

confidence: 86%

“…The final results indicate that this method has good practicality. 20 To sum up, traditional VSLAM algorithm and closed-loop detection algorithm have been able to solve most of these scenarios in a single scene, including constant illumination, view variable and few obstacles. However, today's applications entail addressing increasingly intricate scenes, while also testing the algorithm's capacity for image processing.…”

Section: Related Workmentioning

confidence: 99%

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Local feature matching and tracking optimization of machine VSLAM based on AG Sinkhorn

Pan

2024

Concurrency and Computation

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SummaryAt present, the visual Simultaneous localization and mapping method has a wide range of applications in mobile robots and unmanned driving. However, when the environment changes, the accuracy of the system drops sharply. Aiming at the low matching accuracy of local feature matching and tracking optimization in current visual simultaneous localization and mapping methods, the attention map neural network and Sinkhorn algorithm are used to extract and match the image features. Then, it improves the real‐time performance of the visual simultaneous localization and mapping system by local optimization of feature point matching, and proposes an optimization method for visual simultaneous localization and mapping in view of an end‐to‐end algorithm. The experiment illustrates that in the experimental comparison of HPatches View dataset, the Homography estimation was improved by 0.092, the matching accuracy was improved by 0.087, and the matching recall was improved by 0.038. In the experimental comparison of HPatches Illum dataset, the Homography estimation was improved by 0.015, the matching accuracy is improved by 0.076, and the matching recall was improved by 0.036. The local feature matching and tracking optimization method of the visual Simultaneous localization and mapping system through the attention map neural network and Sinkhorn algorithm can enhance the matching accuracy of local feature matching and tracking optimization in dynamic environment and under changing lighting conditions.

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Section: Related Workmentioning

confidence: 86%

Section: Related Workmentioning

confidence: 99%

Local feature matching and tracking optimization of machine VSLAM based on AG Sinkhorn

Pan

2024

Concurrency and Computation

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“…These depend on some considerations related to the designed parameters of the target system's model. Besides, the analysis of the filter convergence is based on the fact that the sum of two sequences converges weakly to the sum of the limits of those sequences, which follows from a basic result of real analysis on the convergence of sequences of real numbers [40]- [43]. In this paper, those analysis results are extended and applied to the proposed algorithm to verify the convergence of the proposed algorithm.…”

Section: Convergence Of the Pffmentioning

confidence: 99%

Data Fusion With Inverse Covariance Intersection for Prior Covariance Estimation of the Particle Flow Filter

2020

Self Cite

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The prior covariance estimation method based on inverse covariance intersection (ICI) is proposed to apply the particle flow filter. The proposed method has better estimate performance and guarantees consistent estimation results compared with previous works. ICI is the recently developed method of ellipsoidal intersection and is used to get the combined estimate of prior covariance. This method integrates the sample covariance estimate, which is unbiased but usually with high variance, together with a more structured but typically a biased target covariance through fusion gains. For verifying the performance of the proposed algorithm, analysis and simulations are performed. Through the simulations, the results are given to illustrate the consistency and accuracy of the proposed algorithm's estimation and target tracking performance.

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A review of resampling techniques in particle filtering framework

Kuptametee

Aunsri

2022

Measurement

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Ensemble transform particle filter using regularized optimal transport and measure of nonlinearity

Cited by 4 publications

References 21 publications

Local feature matching and tracking optimization of machine VSLAM based on AG Sinkhorn

Local feature matching and tracking optimization of machine VSLAM based on AG Sinkhorn

Data Fusion With Inverse Covariance Intersection for Prior Covariance Estimation of the Particle Flow Filter

A review of resampling techniques in particle filtering framework

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