4.6 Article

Improved Tracking and Docking of Industrial Mobile Robots Through UKF Vision-Based Kinematics Calibration

期刊

IEEE ACCESS
卷 9, 期 -, 页码 127664-127671

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2021.3111004

关键词

Calibration; Kinematics; Wheels; Kalman filters; Visualization; Sensors; Mobile robots; Mobile robot calibration; unscented Kalman filter

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This work focuses on the calibration of geometrical kinematic parameters of a mobile platform using the unscented Kalman filter. During the calibration phase, the mobile platform is externally tracked by a fixed temporary external sensor to retrieve the position of a visual tag.
Performing an open-loop movement, or docking, for an industrial mobile robot (IMR), is a common necessary procedure when relying on environmental sensors is not possible. This procedure precision and outcome, solely depend on the IMR forward kinematic and odometry correctness, which is tied to the kinematics parameters, depending on the IMR kind. Calibrating the kinematic parameters of an IMR is a time consuming and mandatory procedure, since the mechanical tolerances and the assembly procedure may introduce a large variation from the nominal parameters. Furthermore, calibration inaccuracies might introduce severe inconsistencies in tasks such as localization, mapping, and navigation in general. In this work, we focus on the so-called kinematic parameter calibration. We propose the use of the unscented Kalman filter to perform a calibration procedure of the geometrical kinematic parameters of a mobile platform. The mobile platform is externally tracked during the calibration phase, using a fixed temporary external sensor that retrieves the position of a visual tag fixed to the platform. The unscented Kalman filter, using the calibration phase collected data, estimates the enlarged system state, which is comprised of the parameters that have to be estimated, the platform odometry and the visual tag position. The method can either be used online, to identify parameters and monitor their value while the system is operating, or offline, on logged data. We validate this method on two different devices, a 4 mecanum-wheel IMR, and a Turtlebot 3, using a camera to track the movement trough a reference chessboard, for then comparing the original path to its corrected version.

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