Nonlinear observability analysis of multi-robot cooperative localization

Hao, Ning; He, Fenghua; Xie, Changsheng; Tian, Chungeng; Yao, Yu

doi:10.1016/j.sysconle.2022.105340

Cited by 4 publications

(2 citation statements)

References 23 publications

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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%

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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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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%