Ellipsoid SLAM: a novel set membership method for simultaneous localization and mapping

被引：22
|
作者：
Yu, Wen [1 ]
Zamora, Erik [1 ]
Soria, Alberto [1 ]
机构：
[1] CINVESTAV IPN, Natl Polytech Inst, Dept Control Automat, Mexico City 07360, DF, Mexico
来源：
AUTONOMOUS ROBOTS | 2016年 / 40卷 / 01期
关键词：
SLAM; Ellipsoid; Set membership; Convergence; Stability; SYSTEM-IDENTIFICATION; INFORMATION; ALGORITHM; FILTER; LOOP;
D O I：
10.1007/s10514-015-9447-y
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The extended Kalman filter (EKF) simultaneous localization and mapping ( SLAM) requires the uncertainty to be Gaussian noise. This assumption can be relaxed to bounded noise by the set membership SLAM. However, the published set membership SLAMs are not suitable for large-scale and online problems. In this paper, we use ellipsoid algorithm for solving SLAM problem. The proposed ellipsoid SLAM has advantages over EKF SLAM and the other set membership SLAMs, in noise condition, online realization, and large-scale problem. By bounded ellipsoid technique, we analyze the convergence and stability of the ellipsoid SLAM. Simulation and experimental results show that the proposed ellipsoid SLAM is effective for online and large-scale problems such as Victoria Park dataset.
引用
收藏
页码：125 / 137
页数：13
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