Re-estimate the Robot Position by Estimating the Location of Unknown ArUco Markers with Feature Velocity Aid

Abstract:

Indoor navigation stands out as a crucial technology for the smooth performance of robots. This paper proposes an algorithm for position estimation employing a monocular camera and an inertial measurement unit, presenting a system that re-estimates the robot’s position based on the localization of ArUco markers. The algorithm is designed based on the extended Kalman filter, utilizing measurements derived from the calculated position and velocity information obtained from the markers. Experimental validation of the proposed algorithm demonstrates its effectiveness, confirming the ability to estimate the robot’s position by randomly attaching markers in an environment without pre-set configurations.