Multi-sensor fusion for mobile robot indoor localization based on a set-membership estimator
ZHOU Bo
QIAN Kun
MA Xu-dong
DAI Xian-zhong
Abstract：The robust long-distance localization problem of indoor mobile robots is studied in this paper. Taking into account the defects produced by using only a single localization means, a 2D laser scanner and an odometer are adopted as the main localization devices, with their data fused to achieve precise localization of the mobile robot. An improved iterative closest point (ICP) algorithm based on the point-line matching approach is proposed to estimate the relative pose transformation of the robot from point clouds collected by the laser scanner, and the underlying uncertainties for the pose estimation are also derived as a conservative envelope matrix. Through establishing the localization process and measure-ment models, the extended nonlinear set membership filtering (ESMF) is introduced as a multi-sensor fusion method to correct the cumulative errors of the odometer with the scan matching data, and the boundary estimations of pose uncer-tainties are also obtained. Experimental results show that the accuracy, the real-time property and the robustness of the proposed indoor localization system can be guaranteed.
Keywords：mobile robotlocalizationscan matchingset member filterdata fusion
Publication Date：2017-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：10( 541-550 )