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Single Beacon-Based Localization With Constraints and Unknown Initial Poses
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DOI：10.1109/TIE.2015.2499253.png)
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摘要
En 中文
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.
Keyword:
Extended Kalman filter (EKF)
localization
semidefinite programming (SDP)
state estimation
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IF:
7.2
论文数:
1.8W
被引数:
9.8W
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