Mobile robots cooperation with biased exteroceptive measurements

Lassoued, Khaoula; Stanoi, Oana; Fantoni, Isabelle

doi:10.1109/icarcv.2014.7064595

Cited by 6 publications

(4 citation statements)

References 12 publications

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“…are m relative estimate boxes that are calculated based on relative measurements provided by m neighbors when m ≥ 2. Since the complexity of the ICP algorithm depends on the dimension of the box, for the purpose of reducing the computation burden, the ICP method is used to calculate the position on both x and y axes by processing multiple relative estimates for each vehicle in our paper, which reduces the box dimension in [22,23]. The result x R i obtained by the ICP method for all relative estimates can be denoted as follows:…”

Section: Interval Constraint Satisfaction Problem (Icsp) and Interval...mentioning

confidence: 99%

Interval Split Covariance Intersection Filter: Theory and Its Application to Cooperative Localization in a Multi-Sensor Multi-Vehicle System

Shan,

Cabani,

Chafouk

2024

Sensors

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The data incest problem causes inter-estimate correlation during data fusion processes, which yields inconsistent data fusion results. Especially in the multi-sensor multi-vehicle (MSMV) system, the data incest problem is serious due to multiple relative position estimations, which not only lead to pessimistic estimation but also cause additional computational overhead. In order to address the data incest problem, we propose a new data fusion method termed the interval split covariance intersection filter (ISCIF). The general consistency of the ISCIF is proven, serving as supplementary proof for the split covariance intersection filter (SCIF). Moreover, a decentralized MSMV localization system including absolute and relative positioning stages is designed. In the absolute positioning stage, each vehicle uses the ISCIF algorithm to update its own position based on absolute measurements. In the relative position stage, the interval constraint propagation (ICP) method is implemented to preprocess multiple relative position estimates and initially prepare input data for ISCIF. Then, the proposed ISCIF algorithm is employed to realize relative positioning. In addition, comparative simulations demonstrate that the proposed method can achieve both accurate and consistent results compared with the state-of-the-art methods.

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Section: Interval Constraint Satisfaction Problem (Icsp) and Interval...mentioning

confidence: 99%

Interval Split Covariance Intersection Filter: Theory and Its Application to Cooperative Localization in a Multi-Sensor Multi-Vehicle System

Shan,

Cabani,

Chafouk

2024

Sensors

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“…We have noticed that the cooperative method looses sometimes its consistency as shown by the experiments. Therefore, ongoing work is on the implementation of set-membership methods to deal with this issue [11] to get reliable confidence domains.…”

Section: Discussionmentioning

confidence: 99%

Mutual Localization and Positioning of Vehicles Sharing GNSS Pseudoranges: Sequential Bayesian Approach and Experiments

Lassoued¹,

Fantoni²

2015

2015 IEEE 18th International Conference on Intelligent Transportation Systems

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Abstract-In many cooperative Intelligent Transportation Systems (ITS) applications, absolute positioning and relative localization are key issues. When vehicles share GNSS positions, there are often non negligible common-mode errors due mainly to GNSS signal propagation and inaccurate ephemeris data. Cooperative observation techniques allow estimating common biases on the measured pseudodistances to correct these errors and to increase absolute positioning and relative localization accuracy. After having studied some structural properties of the problem in its general form, a low computational cooperative tightly-coupled approach is proposed using sequential Kalman filtering and convex data fusion. As a case study, we consider two vehicles, which cooperate and exchange information in such a way that each vehicle can track the partner's position and improves its absolute position by merging common biases estimates. Experimental results are presented to illustrate the performance of the proposed approach in comparison with a classic standalone method.

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“…Contracting H means replacing [x] by a smaller domain [x ] such that the solution set remains unchanged. The used contractor strategy consists in Forward-Backward Propagation [15]. The CSP consists then in applying contractors using Waltz's algorithm [16].…”

Section: Set Inversion With Cspmentioning

confidence: 99%

Cooperative localization of vehicles sharing GNSS pseudoranges corrections with no base station using set inversion

Lassoued¹,

Fantoni²

2016

2016 IEEE Intelligent Vehicles Symposium (IV)

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Fully distributed localization methods with no central server are relevant for autonomous vehicles that need real-time cooperation. In this paper, mobile vehicles share estimates of GNSS pseudoranges common errors also known as biases. The biases that affect the pseudoranges are mainly due to signal propagation and inaccurate ephemeris data. By describing the measurements models as geometric constraints on intervals, cooperative localization turns into distributed set inversion problem. The solution of this problem is guaranteed to contain the true vehicles positions. We consider vehicles which cooperate and exchange information in order to improve the absolute and relative estimation by fusing pseudoranges corrections shared between them. Results using real measurements are presented to illustrate the performance of the proposed approach in comparison with a standalone method.

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Mobile robots cooperation with biased exteroceptive measurements

Cited by 6 publications

References 12 publications

Interval Split Covariance Intersection Filter: Theory and Its Application to Cooperative Localization in a Multi-Sensor Multi-Vehicle System

Interval Split Covariance Intersection Filter: Theory and Its Application to Cooperative Localization in a Multi-Sensor Multi-Vehicle System

Mutual Localization and Positioning of Vehicles Sharing GNSS Pseudoranges: Sequential Bayesian Approach and Experiments

Cooperative localization of vehicles sharing GNSS pseudoranges corrections with no base station using set inversion

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