A Clutter-Resistant SLAM Algorithm for Autonomous Guided Vehicles in Dynamic Industrial Environment

In dynamic and complicated industrial environments, feature-based SLAM based on laser scanner is a popular choice to achieve localization of autonomous guided vehicles. However, there are many clutters and dynamic objects degrading SLAM performance. This paper proposes a clutter-resistant SLAM solution where both point features generated from reflectors and line features are employed to improve SLAM robustness. First, a point feature recognition method based on geometrical characteristics of reflectors is developed to filter out clutters and identify true reflector landmarks; Then a dual-map based map management scheme is proposed for EKF-SLAM to further eliminate both types of fallacious landmarks and enhance its clutter resistance capability. The proposed methods eliminate adverse impact of clutters and thus improve SLAM performance in terms of accuracy, consistency and efficiency. The effectiveness of the proposed clutter-resistant SLAM solution is validated through real-time experiments. The absolute localization error is controlled within 19 mm and 31mm in X-axis and Y-axis respectively. The improved SLAM algorithm is proved to be accurate and efficient enough for practical application in dynamic and complicated industrial environments.