Abstract
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.
Highlights
Simultaneous localization and mapping (SLAM) is a fundamental problem in the field of robotics research [1], which has been regarded as a ‘‘holy grail’’ [2]
The main purpose of this study is to present effective techniques for eliminating fallacious landmarks in cluttered industrial environments, which contributes to a better SLAM performance in terms of EKF convergency, accuracy and efficiency
EXPERIMENTAL RESULTS The effectiveness of the proposed landmark recognition method and dual-map based EKF-SLAM solution are validated through practical experiments in a manufacturing factory, which spans an area of approximately 82.5 × 52 m2 with complex components such as autonomous devices, storage racks and an office etc
Summary
Simultaneous localization and mapping (SLAM) is a fundamental problem in the field of robotics research [1], which has been regarded as a ‘‘holy grail’’ [2]. A SLAM solution enables a mobile robot to incrementally build an environment map while localizing itself without any priori knowledge of the environment. Autonomous guided vehicle (AGV) is an important branch of mobile robots which is widely applied to transport heavy goods in industry [3]. The SLAM problem for AGV requires higher robustness and accuracy due to complicated and dynamic industrial environments and the demands for precise loading or unloading operation. In the past thirty years, many notable achievements for SLAM problem have been obtained [4]–[7]. Estimation-theoretic approaches, based on Bayesian estimation framework, have been
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