Recursive Variational Bayesian Inference to Simultaneous Registration and Fusion

Zhu, Hao; Hu, Jinsong; Leung, Henry; Zhang, Bin

doi:10.5772/64012

Cited by 3 publications

(3 citation statements)

References 19 publications

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“…It can be seen that the tracking error gradually decreases with the increased value of

at the initial stage. However, when

is too large, the tracking error will increase due to the excessive self-pruning of the parameters by the VB algorithm [ 32 ]. Therefore, in the simulation, we set

.…”

Section: Simulation and Discussionmentioning

confidence: 99%

“…To guarantee the conjugate relationship, a solution algorithm based on variational Bayesian (VB) inference is then developed, such that the system state and the noise parameter can be alternatively corrected. VB inference is widely applied in machine learning community [ 29 , 30 ] and has been successfully introduced into target tracking and sensor fusion [ 31 , 32 , 33 ] in recent years. Overall, this paper is interesting from the following aspects…”

Section: Introductionmentioning

confidence: 99%

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Multi-Vehicle Cooperative Target Tracking with Time-Varying Localization Uncertainty via Recursive Variational Bayesian Inference

Chen

Wang

Chen

et al. 2020

Sensors

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Cooperative target tracking by multiple vehicles connected through inter-vehicle communication is a promising way to improve the estimation of target state. The effectiveness of cooperative tracking closely depends on the accuracy of relative localization between host and cooperative vehicles. However, the localization signal usually provided by the satellite-based navigation system is rather susceptible to dynamic driving environment, thus influencing the effectiveness of cooperative tracking. In order to implement reliable cooperative tracking, especially when the statistical characteristic of the relative localization noise is time-varying and uncertain, this paper presents a recursive Bayesian framework which jointly estimates the state of the target and the cooperative vehicle as well as the localization noise parameter. An online variational Bayesian inference algorithm is further developed to achieve efficient recursive estimate. The simulation results verify that our proposed algorithm can effectively boost the accuracy of target tracking when the localization noise dynamically changes over time.

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“…It can be seen that the tracking error gradually decreases with the increased value of

at the initial stage. However, when

is too large, the tracking error will increase due to the excessive self-pruning of the parameters by the VB algorithm [ 32 ]. Therefore, in the simulation, we set

.…”

Section: Simulation and Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-Vehicle Cooperative Target Tracking with Time-Varying Localization Uncertainty via Recursive Variational Bayesian Inference

Chen

Wang

Chen

et al. 2020

Sensors

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show abstract

“…24 These approaches have the benefit of augmenting the filter with latent variables to increase accuracy of the assumptions in the model. In Zhu et al's work, 27 sensor biases are estimated with Variational Bayesian algorithm and in Caron et al's work, 28 the switching observation model is used to recover from changing sensor states. While our approach is also a filter level approach, it models the feature quality based on the environment and weighs them in a complementary way.…”

Section: Introductionmentioning

confidence: 99%

Autonomous feature type selection based on environment using expectation maximization in self-localization

Özkucur

Akın

2018

International Journal of Advanced Robotic Systems

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Self-localization in autonomous robots is one of the fundamental issues in the development of intelligent robots, and processing of raw sensory information into useful features is an integral part of this problem. In a typical scenario, there are several choices for the feature extraction algorithm, and each has its weaknesses and strengths depending on the characteristics of the environment. In this work, we introduce a localization algorithm that is capable of capturing the quality of a feature type based on the local environment and makes soft selection of feature types throughout different regions. A batch expectation–maximization algorithm is developed for both discrete and Monte Carlo localization models, exploiting the probabilistic pose estimations of the robot without requiring ground truth poses and also considering different observation types as blackbox algorithms. We tested our method in simulations, data collected from an indoor environment with a custom robot platform and a public data set. The results are compared with the individual feature types as well as naive fusion strategy.

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Distributed Target Tracking Method Based on Variational Bayesian with Systematic Biases

Jin

Zhang

Liu

et al. 2022

2022 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC)

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Recursive Variational Bayesian Inference to Simultaneous Registration and Fusion

Cited by 3 publications

References 19 publications

Multi-Vehicle Cooperative Target Tracking with Time-Varying Localization Uncertainty via Recursive Variational Bayesian Inference

Multi-Vehicle Cooperative Target Tracking with Time-Varying Localization Uncertainty via Recursive Variational Bayesian Inference

Autonomous feature type selection based on environment using expectation maximization in self-localization

Distributed Target Tracking Method Based on Variational Bayesian with Systematic Biases

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