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Advisor(s)
Abstract(s)
Autonomous mobile robots applications require a
robust navigation system, which ensures the proper movement
of the robot while performing their tasks. The key challenge
in the navigation system is related to the indoor localization.
Simultaneous Localization and Mapping (SLAM) techniques
combined with Adaptive Monte Carlo Localization (AMCL)
are widely used to localize robots. However, this approach is
susceptible to errors, especially in dynamic environments and
in presence of obstacles and objects. This paper presents an
approach to improve the estimation of the indoor pose of
a wheeled mobile robot in an environment. To this end, the
proposed localization system integrates the AMCL algorithm
with the position updates and corrections based on the artificial
vision detection of fiducial markers scattered throughout the
environment to reduce the errors accumulated by the AMCL
position estimation. The proposed approach is based on Robot
Operating System (ROS), and tested and validated in a simulation
environment. As a result, an improvement in the trajectory
performed by the robot was identified using the SLAM system
combined with traditional AMCL corrected with the detection,
by artificial vision, of fiducial markers.
Description
Keywords
Computer vision Indoor positioning systems
Citation
Oliveira Junior, Alexandre de; Piardi, Luis; Bertogna, Eduardo Giometti; Leitao, Paulo (2021). Improving the mobile robots indoor localization system by combining slam with fiducial markers. In 18thLatin American Robotics Symposium, 13th Brazilian Symposium on Robotics, and 12th Workshop on Robotics in Education, LARS-SBR-WRE 2021. p. 234-239. ISBN 978-1-6654-0761-8