구조화된 환경에서 QR 코드 기반 센서 융합 기법을 이용한 이동 로봇의 위치 추정 향상
Enhancing Localization of Mobile Robots Using QR Code-Based Sensor Fusion Method in Structured Environment
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초록
Accurate localization and environment mapping are key technologies for the autonomous operation of robots, particularly in indoor environments where GPS is unavailable. Simultaneous Localization and Mapping (SLAM) technology addresses the issue, but it faces sensor drift, which can degrade localization accuracy over time. This paper proposes enhancing SLAM using sensor fusion and QR codes as artificial landmarks. QR codes are low-cost, easily recognizable, and provide global pose information, which helps correct accumulated localization errors. Particularly in indoor environments, we propose constraints to precisely correct the robot’s rotational estimation, based on the assumption that artificial landmarks, such as QR codes, are attached to flat surfaces like walls or pillars. In the experiments, integrating data from a 2D LiDAR, IMU, and a webcam with QR code landmarks improves real-time localization accuracy, offering a reliable solution for indoor robot navigation.
Abstract
Accurate localization and environment mapping are key technologies for the autonomous operation of robots, particularly in indoor environments where GPS is unavailable. Simultaneous Localization and Mapping (SLAM) technology addresses the issue, but it faces sensor drift, which can degrade localization accuracy over time. This paper proposes enhancing SLAM using sensor fusion and QR codes as artificial landmarks. QR codes are low-cost, easily recognizable, and provide global pose information, which helps correct accumulated localization errors. Particularly in indoor environments, we propose constraints to precisely correct the robot’s rotational estimation, based on the assumption that artificial landmarks, such as QR codes, are attached to flat surfaces like walls or pillars. In the experiments, integrating data from a 2D LiDAR, IMU, and a webcam with QR code landmarks improves real-time localization accuracy, offering a reliable solution for indoor robot navigation.