Single Beacon-Based Localization With Constraints and Unknown Initial Poses

This paper studies a single beacon-based three-dimensional multirobot localization (MRL) problem. Unlike most of existing localization algorithms which use extended Kalman filter or maximum a posteriori, moving horizon estimation (MHE), and convex optimization are novelly designed to perform MRL wit...

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Bibliographic Details
Published in:IEEE transactions on industrial electronics (1982) 2016-04, Vol.63 (4), p.2229-2241
Main Authors: Wang, Sen, Gu, Dongbing, Chen, Ling, Hu, Huosheng
Format: Article
Language:English
Subjects:
Algorithm design and analysis
Computational complexity
Estimation
Extended Kalman Filter
Localization
Optimization
Robot sensing systems
Semi-Definite Programming
State Estimation
Time measurement
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Summary:This paper studies a single beacon-based three-dimensional multirobot localization (MRL) problem. Unlike most of existing localization algorithms which use extended Kalman filter or maximum a posteriori, moving horizon estimation (MHE), and convex optimization are novelly designed to perform MRL with constraints and unknown initial poses. The main contribution of this paper is three-fold: 1) a constrained MHE-based localization algorithm, which can bound localization error, impose various constraints and compromise between computational complexity and estimator accuracy, is proposed to estimate robot poses; 2) constrained optimization is examined in the perspective of Fisher information matrix to analyze why and how multirobot information and constraints are able to reduce uncertainties; 3) a semidefinite programming-based initial pose estimation, which can efficiently converge to global optimum, is developed by using convex relaxation. Simulations and experiments are conducted to verify the effectiveness of the proposed methods.
ISSN:0278-0046
1557-9948
DOI:10.1109/TIE.2015.2499253