A state exchange approach in real conditions for multi-robot cooperative localization

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

doi:10.1109/iros.2010.5653406

Cited by 9 publications

(7 citation statements)

References 15 publications

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“…We use a set membership framework that is able to deal with public information and so it is not necessary to manipulate private copies of some information, as done in [1] and [7]. Every information is handled as an interval or a box.…”

Section: Information Exchange Between Robotsmentioning

confidence: 99%

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Mobile robots cooperation with biased exteroceptive measurements

Lassoued

Stanoi

Fantoni

2014

2014 13th International Conference on Control Automation Robotics &Amp; Vision (ICARCV)

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When mobile robots need to cooperate, mutual localization is a key issue. The objective is to enable cooperative localization capabilities, such that each robot determines the partners positions in a common frame with reliable confidence estimates. Exteroceptive sensors can measure distances to known beacons in order to provide absolute information. It often exists biases that affect these measurements because of particular environment conditions or because of an inaccurate knowledge of the beacons positions. In this work, each robot is also equipped with proprioceptive sensors, but no sensor can measure the interdistance between the robots. The method that we consider is fully distributed between the robots, which share positions and biases estimates. In order to handle the data incest problem, we use constraint propagation techniques on intervals. The distributed cooperative localization method gives sets that always contain the true positions of the robots without any over-convergence. Simulation results show that the so-called method improves localization performance compared to standalone methods.

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Section: Information Exchange Between Robotsmentioning

confidence: 99%

“…Moreover, they rely on the hypothesis of error independence and have to address the problem of data-incest. Aufrere et al [7] solved this problem by handling private copies of the knowledge of robot group to address this over-convergence issue.…”

Section: Introductionmentioning

confidence: 99%

Mobile robots cooperation with biased exteroceptive measurements

Lassoued

Stanoi

Fantoni

2014

2014 13th International Conference on Control Automation Robotics &Amp; Vision (ICARCV)

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“…Each vehicle may communicate the raw measurements from sensors [14] instead of a fused pose. Another solution is to use two EKF [13], [2], the first one without the information from the other using only the sensors 1 The authors are with Sorbonne universités, Université de technologie de Compiègne, CNRS, Heudiasyc UMR 7253, France firstname.surname@hds.utc.fr of the vehicle. This first filter estimates the pose of the vehicle for the other, this pose can be communicated.…”

Section: Introductionmentioning

confidence: 99%

Distributed asynchronous cooperative localization with inaccurate GNSS positions

Héry

2019

2019 IEEE Intelligent Transportation Systems Conference (ITSC)

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Localization remains a major issue for autonomous vehicles. Accurate localization relative to the road and other vehicles is essential for many navigation tasks. When vehicles cooperate and exchange information through wireless communications, they can improve mutually their localization. This paper presents a distributed cooperative localization method based on the exchange of Local Dynamic Maps (LDMs). Every LDM contains dynamic information on the pose and kinematic of all the cooperating agents. Different sources of information such as dead-reckoning from the CAN bus, inaccurate (i.e. biased) GNSS positions, LiDAR and road border detections are merged using an asynchronous Kalman filter strategy. The LDMs received from the other vehicles are merged using a Covariance Intersection Filter to avoid data incest. Experimental results are evaluated on platooning scenarios. They show the importance of estimating GNSS biases and having accurate relative measurements to improve the absolute localization process. These results also illustrate that the relative localization between vehicles is improved in every LDMs even for vehicles not able to perceive surrounding vehicles but which are instead perceived by others.

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“…Roumeliotis et al introduced a distributed approach for collective localization based on a centralized EKF [7] and resolved the data incest problem by exchanging only a part of the group state [8] between vehicles. An other approach [9] exploits all the available information in each vehicle and handled private copies of the group knowledge to solve the data incest issue. To do the data fusion, there are different fusion operators depending on the assumptions whether the two estimates to be fused are intercorrelated or not.…”

Section: Introductionmentioning

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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A state exchange approach in real conditions for multi-robot cooperative localization

Cited by 9 publications

References 15 publications

Mobile robots cooperation with biased exteroceptive measurements

Mobile robots cooperation with biased exteroceptive measurements

Distributed asynchronous cooperative localization with inaccurate GNSS positions

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

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