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Sensor fusion for mobile robot localization using extended Kalman filter, UWB ToF and ArUco markers
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Orientador(es)
Resumo(s)
The ability to locate a robot is one of the main features to
be truly autonomous. Different methodologies can be used to determine
robots location as accurately as possible, however these methodologies
present several problems in some circumstances. One of these problems
is the existence of uncertainty in the sensing of the robot. To solve this
problem, it is necessary to combine the uncertain information correctly.
In this way, it is possible to have a system that allows a more robust
localization of the robot, more tolerant to failures and disturbances. This
paper evaluates an Extended Kalman Filter (EKF) that fuses odometry
information with Ultra-WideBand Time-of-Flight (UWB ToF) measurements
and camera measurements from the detection of ArUco markers
in the environment. The proposed system is validated in a real environment
with a differential robot developed for this purpose, and the
achieved results are promising.
Descrição
Palavras-chave
ArUco markers Autonomous mobile robot Extended kalman filter Localization Ultra-wideband Vision based system
Contexto Educativo
Citação
Faria, Sílvia; Lima, José; Costa, Paulo (2021). Sensor fusion for mobile robot localization using extended Kalman filter, UWB ToF and ArUco markers. In Pereira, Ana I.; Fernandes, Florbela P.; Coelho, João Paulo; Teixeira, João Paulo; Pacheco, Maria F.; Alves, Paulo; Lopes, Rui Pedro (Eds.) Optimization, learning algorithms and applications: first International Conference, OL2A 2021. Cham: Springer Nature. p. 235-250. ISBN 978-3-030-91884-2
Editora
Springer Nature