[1]SUN Zifei,QIAN Kun,MA Xudong,et al.Self-localization of mobile robot in dynamic environments based onlocalizability estimation with multi-sensor observation[J].CAAI Transactions on Intelligent Systems,2017,12(4):443-449.[doi:10.11992/tis.201607007]
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Self-localization of mobile robot in dynamic environments based onlocalizability estimation with multi-sensor observation

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 12 Number of periods: 2017 4 Page number: 443-449 Column: 学术论文—智能系统 Public date: 2017-08-25
Title:
Self-localization of mobile robot in dynamic environments based onlocalizability estimation with multi-sensor observation
Author(s):
SUN Zifei1;  QIAN Kun1; 2;  MA Xudong1; 2;  DAI Xianzhong1; 2
1. School of Automation, Southeast University, Nanjing 210096, China;
2. Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of Education, Nanjing 210096, China
Keywords:
dynamic environment; self-localization; RGB-D sensor; Fisher information matrix; people-detecting; localizability; online estimation; mobile robot
CLC:
TP24
DOI:
10.11992/tis.201607007
Abstract:
Based on the localizability estimation theory, in this paper, we propose a new method for the reliable self-localization of mobile robots in a disturbed dynamic indoor environment by the adoption of an RGB-D sensor to assist the laser scanner. People’s location areas are rapidly detected in RGB-D data, which are then transformed to the laser sensor coordinate to compute the influence of the dynamic obstacles on the laser data. In combination with the discrete Fisher information matrix, we estimate the localizability matrix of the observation information online. In addition, we assess the reliability of the information in odometers by the covariance matrix of the prediction model, thereby dynamically compensating for the effect of the observation information on the particle set. We conducted experiments in a dynamic indoor environment and the results confirm the accuracy and reliability of the proposed robot localization method.
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