Resilient and Consistent Multirobot Cooperative Localization With Covariance Intersection

Chang, Tsang-Kai; Chen, Kenny; Mehta, Ankur

doi:10.1109/tro.2021.3104965

Cited by 23 publications

(11 citation statements)

References 38 publications

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“…This behavior is also apparent in TABLEI, where GLA-LDL reduced RT − LAE of standalone position source by 45%, with respect to 24% of GLA, 42% of GLLMS and 43% of GLLME. It also proves the benefits of combining the overlapping estimations of vehicles following(10), since the without combination step GLA is much less accurate. Increasing p to 10% and 20%, Fig.4-(b),(c) prove that global awareness is totally unable to converge, significantly increasing ALAE beyond 20m.…”

mentioning

confidence: 70%

“…2,i . Note that [16] proves that the combination step (10) derives exactly from the smoothness term if we minimize (4) with respect to x(t) i . Therefore, vehicle i receives the relative observations and self positions from its neighbors, defines the Laplacian matrix of star topology and estimates the intermediate location vector following the adaptation equation (9).…”

Section: A Graph Laplacian and Lms Based Solutionmentioning

confidence: 97%

“…The requirement for all-to-all V2V communication at the initialization stage is a limitation that should be addressed. Covariance intersection (CI) has been employed in the CA scenario of a multi-robot group in [10]. Each robot makes use of an Extended KF (EKF), exploiting relative observations to landmarks and other robots.…”

Section: Introductionmentioning

confidence: 99%

“…To further boost the performance of CA we propose a local diffusion based approach, which addresses the limitations of global awareness solution related to lacking knowledge about other networked vehicles and convergence speed. The proposed framework is linear with respect to its parameters (instead of [10] and [11]), totally distributed, and doesn't depend on fixed communication topologies.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Robustifying cooperative awareness in autonomous vehicles through local information diffusion

Piperigkos

Lalos

Berberidis

2022

2022 IEEE 20th International Conference on Industrial Informatics (INDIN)

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Cooperative Intelligent Transportation Systems envision the integration of cooperative intelligence as a key operational part of autonomous driving. In this way, a fleet or swarm of Connected and Automated Vehicles collectively coordinates its driving actions in order to maximize its performance. To realize this ambition, vehicles need to be fully location-aware of their surrounding environment, through distributed AI intelligence. Motivated by this requirement, we develop in this paper a distributed cooperative awareness scheme which performs multimodal fusion of heterogeneous sensor sources along with V2V communication information, using graph Laplacian matrix and Least-Mean-Squares algorithm. The intuition behind our approach is that neighboring vehicles are interested in estimating common positions of other vehicles. We build upon our previous work on global awareness though local information diffusion, and prove that the proposed distributed framework is able to address highly efficient the case of lacking any information about other networked vehicles. More specifically, our approach achieves high enough convergence speed as well as location accuracy. The evaluation study has been performed in CARLA autonomous driving simulator and verifies the proposed method's benefits over other related solutions.

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mentioning

confidence: 70%

Section: A Graph Laplacian and Lms Based Solutionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Robustifying cooperative awareness in autonomous vehicles through local information diffusion

Piperigkos

Lalos

Berberidis

2022

2022 IEEE 20th International Conference on Industrial Informatics (INDIN)

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show abstract

“…The localization estimation utilizing shared information fusion has been improved by using an estimator structure that takes advantage of covariance intersection (CI) to reduce one source of measurement correlation while properly including others. Similarly, a work [29] developed a CI-based localization method with an explicit communication update and guaranteed estimation consistency simultaneously would increase the robustness of multi-robot cooperative localization methods in a dispersed setting. However, robots keep a system-wide estimate in their algorithm, which relative observations can instantly update.…”

Section: Related Workmentioning

confidence: 99%

DGORL: Distributed Graph Optimization based Relative Localization of Multi-Robot Systems

Latif¹,

Parasuraman²

2022

Preprint

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An optimization problem is at the heart of many robotics estimating, planning, and optimum control problems. Several attempts have been made at model-based multi-robot localization, and few have formulated the multi-robot collaborative localization problem as a factor graph problem to solve through graph optimization. Here, the optimization objective is to minimize the errors of estimating the relative location estimates in a distributed manner. Our novel graph-theoretic approach to solving this problem consists of three major components; (connectivity) graph formation, expansion through transition model, and optimization of relative poses. First, we estimate the relative pose-connectivity graph using the received signal strength between the connected robots, indicating relative ranges between them. Then, we apply a motion model to formulate graph expansion and optimize them using g 2 o graph optimization as a distributed solver over dynamic networks. Finally, we theoretically analyze the algorithm and numerically validate its optimality and performance through extensive simulations.The results demonstrate the practicality of the proposed solution compared to a state-of-the-art algorithm for collaborative localization in multi-robot systems.

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Event‐triggered resilient joint mobile robot localization and sensor fault estimation

Lu,

Karimi,

et al. 2024

Intl J Robust & Nonlinear

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SummaryThe event‐triggered joint sensor fault estimation and mobile robot (MR) localization (MRL) problem (MRLP) subject to the potential fluctuations of the estimator gain are investigated. From the external sensor to the estimator, to decrease the consumption of sensor energy and cut down network bandwidth resources, an event‐triggered scheme is considered. For the sake of characterizing the phenomenon induced by the inaccurate calculation of estimator gain, an uncertainty with the bounded second moment is employed. The purpose of this study is theoretically to find a feasible and effective approach to the addressed joint estimation problem such that the estimation error (EE) covariance (EEC) meets the given performance index. First, a minimum upper bound (UB) of the EEC is derived. Subsequently, in terms of the proposed joint estimation approach and the corresponding results obtained, an algorithm to address the resilient joint sensor fault estimation and MRLP is summarized. At the end, the effectiveness of the proposed algorithm is validated through conducting a set of comparisive experiments.

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Resilient and Consistent Multirobot Cooperative Localization With Covariance Intersection

Cited by 23 publications

References 38 publications

Robustifying cooperative awareness in autonomous vehicles through local information diffusion

Robustifying cooperative awareness in autonomous vehicles through local information diffusion

DGORL: Distributed Graph Optimization based Relative Localization of Multi-Robot Systems

Event‐triggered resilient joint mobile robot localization and sensor fault estimation

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