An Exploration of Moving Robot Localization Assisted with a Static Monocular Camera

Abstract

Simultaneous localization and mapping (SLAM) is critical for robots in exploring an unknown environment. The monocular camera mounted on the robot can capture images continuously. However, the localization and mapping process may fail when there are not enough structure features observed from the moving camera on the robot. In this paper, we explore to use an external static surveillance camera to calculate the realtime pose data for the moving robot. We perform an adaptive self-localization for the robot taking advantage the joint information both from the camera on the robot and the external static surveillance camera. The localization results from this coordination are fused to solve the problem that localization may be unreliable in the SLAM. Whenever the SLAM fails, the estimated poses from the other camera can effectively help with the localization for the moving robot. We set up an environment to perform the experiments and validate the feasibility of coordinated mining of multiple cameras. The results can be beneficial for autonomous driving and the deployment of intelligent infrastructures.