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Mobile robot navigation using grid line patterns via probabilistic measurement modeling

Authors
Kim, TaeyunKim, JinwhanChoi, Hyun-Taek
Issue Date
4월-2016
Publisher
SPRINGER HEIDELBERG
Keywords
Indoor navigation; Grid line patterns; Particle filter; Mobile robot
Citation
INTELLIGENT SERVICE ROBOTICS, v.9, no.2, pp 141 - 151
Pages
11
Journal Title
INTELLIGENT SERVICE ROBOTICS
Volume
9
Number
2
Start Page
141
End Page
151
URI
https://www.kriso.re.kr/sciwatch/handle/2021.sw.kriso/672
DOI
10.1007/s11370-015-0191-0
ISSN
1861-2776
1861-2784
Abstract
Mobile robots are generally equipped with proprioceptive motion sensors such as odometers and inertial sensors. These sensors are used for dead-reckoning navigation in an indoor environment where GPS is not available. However, this dead-reckoning scheme is susceptible to drift error in position and heading. This study proposes using grid line patterns which are often found on the surface of floors or ceilings in an indoor environment to obtain pose (i.e., position and orientation) fix information without additional external position information by artificial beacons or landmarks. The grid lines can provide relative pose information of a robot with respect to the grid structure and thus can be used to correct the pose estimation errors. However, grid line patterns are repetitive in nature, which leads to difficulties in estimating its configuration and structure using conventional Gaussian filtering that represent the system uncertainty using a unimodal function (e.g., Kalman filter). In this study, a probabilistic sensor model to deal with multiple hypotheses is employed and an online navigation filter is designed in the framework of particle filtering. To demonstrate the performance of the proposed approach, an experiment was performed in an indoor environment using a wheeled mobile robot, and the results are presented.
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