Graph-based observability analysis for mutual localization in multi-robot systems

Hao, Ning; He, Fenghua; Yi, Hou; Yao, Yu

doi:10.1016/j.sysconle.2022.105152

Cited by 9 publications

(4 citation statements)

References 9 publications

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“…Particularly, graph optimization and factoring have been recently proposed in the literature to solve different variants of the MRL problem [22,23,24]. Even though the issue of reducing the complexity of graph optimization has recently been addressed [25,26], to the best of our knowledge, little work has yet explicitly taken into account estimation consistency (i.e., unbiased estimates and an estimated covariance more significant than or equal to the actual covariance [27]) in the design of graph reduction (sparsification) schemes.…”

Section: Related Workmentioning

confidence: 99%

“…Therefore, this paper proposes a graph-based optimization algorithm to address the gaps mentioned above. To form a graph using shared RSSI information among robots, we employ a Relative Pose Measurement Graph (RPMG) using observability analysis [23]. Once we have created a connected, reliable graph, we exploit particle filtering over the motion model to expand the graph based on mobility constraints.…”

Section: Related Workmentioning

confidence: 99%

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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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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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

Latif¹,

Parasuraman²

2022

Preprint

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“…Since the absolute pose information is not available, the non-linear system used by EKF-based CL has three unobservable dimensions, i.e., the absolute position and the orientation [21], [22]. [18] pointed out that the linearized error-state system (4) evaluated at the latest state estimates used by EKF-based CL has only two unobservable dimensions.…”

Section: B Linearized Error-state System Of Ekf-based CLmentioning

confidence: 99%

“…Proof: It has been proved that the observable dimension of the nonlinear CL system is 3N −3 in [21] and [22]. Since F k−1 = I 3N ×3N , the dimension of the observable subspace of system ( 13) is equal to the rank of H k .…”

Section: B Transformed Linearized Error-state Systemmentioning

confidence: 99%

Multi-robot Cooperative Localization Using Anonymous Relative-Bearing Measurements

Tian

Hao

2022

2022 41st Chinese Control Conference (CCC)

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This paper considers the problem of distributed cooperative localization (CL) via robot-to-robot measurements for a multi-robot system. We propose a distributed consistent CL algorithm. The key idea is to perform the EKF-based state estimation in a transformed coordinate system. Specifically, a coordinate transformation is constructed by decomposing the state-propagation Jacobian by which the correct observability properties are guaranteed. Moreover, the transformed statepropagation Jacobian becomes an identity matrix which is more suitable for distribution. In the proposed algorithm, a serverbased framework is adopted to distributely estimate the robot pose in which each robot propagates its pose estimations and the server maintains the correlations. To reduce communication costs, only when the multi-robot system takes a robot-to-robot relative measurement, the robots and the server exchange information to update the pose estimations and the correlations. In addition, no assumptions are made about the type of robots or relative measurements. The proposed algorithm has been validated by experiments and shown to outperform the stateof-art algorithms in terms of consistency and accuracy.

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DGORL: Distributed Graph Optimization Based Relative Localization of Multi-robot Systems

Latif,

Parasuraman

2024

Springer Proceedings in Advanced Robotics

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Graph-based observability analysis for mutual localization in multi-robot systems

Cited by 9 publications

References 9 publications

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

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

Multi-robot Cooperative Localization Using Anonymous Relative-Bearing Measurements

DGORL: Distributed Graph Optimization Based Relative Localization of Multi-robot Systems

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