Drift-Free Visual SLAM for Mobile Robot Localization by Integrating UWB Technology

Abstract

Visual simultaneous localization and mapping (vSLAM or visual SLAM) is an important technique for mobile robot localization in the global navigation satellite system denied (GNSS-denied) environments. However, the positioning accuracy could become unbearable due to the lack of image feature points when the robot navigates in a spacious indoor space. The drifting errors accumulated over time are generally inevitable and need to be mitigated by more sophisticated loop-closure algorithms. In this paper, we propose a drift-free visual SLAM technique for mobile robot localization by integrating the ultra-wideband (UWB) positioning technology. The basic concept is to utilize the global constraint of the UWB positioning to reduce the locally accumulated errors of visual SLAM localization based on the extended Kalman filtering (EKF) framework. In our experimental results, various SLAM approaches are performed in the indoor scenes, and the evaluation and comparison have demonstrated the feasibility of the proposed localization technique. By the integration of UWB positioning, the overall drift error of the robot navigation is reduced for more than 50%.