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

Abstract

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.

Keywords

• Mobile robot calibration
• unscented Kalman filter