Cooperative Multi-Vehicle Localization

Karam, Nadir; Chausse, Frédéric; Aufrère, Romuald; Chapuis, Roland

doi:10.1109/ivs.2006.1689688

Cited by 37 publications

(28 citation statements)

References 16 publications

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“…All the participating vehicles or robots send their measurements to this FC. EKF [5], [7], Particle Filter, MLE [3] are some of the techniques used by these FC to fuse data.…”

Section: Problem Descriptionmentioning

confidence: 99%

“…Many solutions already exist which take advantage of such CL such as Extended Kalman Filter (EKF) [5], [7], Maximum Likelihood Estimation (MLE) [3], Maximum A Posteriori Estimation (MAP) [4], Markov Localization [6], split covariance intersection filter [8], random finite set framework [10] and Symmetric Measurement Equation (SME) Filter [11]. All these solutions provide novel ways of solving the problem, but still lack one or more of the following points:…”

Section: Introductionmentioning

confidence: 99%

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Vehicle infrastructure cooperative localization using Factor Graphs

Gulati

Zhang

Clarke

et al. 2016

2016 IEEE Intelligent Vehicles Symposium (IV)

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Abstract-Highly assisted and Autonomous Driving is dependent on the accurate localization of both the vehicle and other targets within the environment. With increasing traffic on roads and wider proliferation of low cost sensors, a vehicleinfrastructure cooperative localization scenario can provide improved performance over traditional mono-platform localization. The paper highlights the various challenges in the process and proposes a solution based on Factor Graphs which utilizes the concept of topology of vehicles. A Factor Graph represents probabilistic graphical model as a bipartite graph. It is used to add the inter-vehicle distance as constraints while localizing the vehicle. The proposed solution is easily scalable for many vehicles without increasing the execution complexity. Finally simulation indicates that incorporating the topology information as a state estimate can improve performance over the traditional Kalman Filter approach.

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“…All the participating vehicles or robots send their measurements to this FC. EKF [5], [7], Particle Filter, MLE [3] are some of the techniques used by these FC to fuse data.…”

Section: Problem Descriptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Vehicle infrastructure cooperative localization using Factor Graphs

Gulati

Zhang

Clarke

et al. 2016

2016 IEEE Intelligent Vehicles Symposium (IV)

View full text Add to dashboard Cite

show abstract

“…However, as it is not the situation and there can be sensory errors and delays and/or packet drops in communication, EKF can give better estimates and even can handle the localization problem during commun ication issues without divergence. It will keep giving a clear picture of the states of all the agents, as the agents not only can localize themselves but also their neighbors, the neighborhood information can be used to estimate the position as the whole group [14]. The majo r disadvantage of this approach is that it requires a lot of co mmunicat ion among the agents for successful operation, and increasing the number of agents will choke the network.…”

Section: Kalman Filteringmentioning

confidence: 99%

An Overview of Localization Methods for Multi-Agent Systems

Saim¹,

Munawar²,

Al‐Saggaf³

2017

IJERA

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Localization of mult i-agent systems is a fundamental requirement for mu lti-agent systems to operate and cooperate properly. The problem of localization can be divided into two categories; one in which a -p riori informat ion is available and the second where the global position is to be asce rtained without a-priori informat ion. This paper gives a comprehensive survey of localization techniques that exist in the literature for both the categories with the objectives of knowing the current state -of-the-art, helping in selecting the proper approach in a given scenario and promoting research in this area. A detailed description of methods that exist in the literature are provided in considerable detail. Then these methods are compared, and their weaknesses and strengths are discussed. Finally, so me future research recommendations are drawn out of this survey.

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“…Hence we did not use the Odometry for our final comparison for GLMB Filter. Further we also compare with two other covariances of the RADAR as [3,3] and [5,5].…”

Section: A Simulation Setupmentioning

confidence: 99%

“…[2] and [3] use Kalman Filter and its derivatives to perform the CL. Other researches provided novel solutions including Maximum A Posteriori Estimation (MAP) [4], Particle Filters [5], Markov localization [6], Split Covariance Intersection Filter [7], and Random Finite Set framework (RFS) [8].…”

Section: Introductionmentioning

confidence: 99%

Data association — solution or avoidance: Evaluation of a filter based on RFS framework and factor graphs with SME

Gulati

Sharif

Zhang

et al. 2017

2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)

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Abstract-Data or measurement-to-track association is an integral and expensive part of any solution performing multitarget multi-sensor Cooperative Localization (CL) for better state estimation. Various performance evaluations have been performed between various state-of-the-art solutions, but they have been often limited within same family of algorithms. However, there exist solutions which avoid the task of data association to perform the CL in a multi-target multi-sensor environment. Factor Graphs using Symmetric Measurement Equations (SMEs) factor is one such solution. In this paper we compare and contrast the state estimation using state-ofthe-art Random Finite Set (RFS) approach and using a Factor Graph solution with SMEs. For a RFS we use multi-sensor multi-object with the Generalized Labeled Multi-Bernoulli (GLMB) Filter. These two solution use conceptually different approaches, GLMB Filter solves the data association implicitly, but Factor Graph based solution avoids the task altogether. Simulations present an interesting results where for simple scenarios implemented GLMB filter performs efficiently. But the performance of GLMB Filter degrades faster than Factor Graphs using SMEs when the error in the sensors increase.

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Cooperative Multi-Vehicle Localization

Cited by 37 publications

References 16 publications

Vehicle infrastructure cooperative localization using Factor Graphs

Vehicle infrastructure cooperative localization using Factor Graphs

An Overview of Localization Methods for Multi-Agent Systems

Data association — solution or avoidance: Evaluation of a filter based on RFS framework and factor graphs with SME

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