김명수 장근우 박재흥 2024-01-19T08:04:40Z 2024-01-19T08:04:40Z 2023-11-30 2023-11 1975-6291 https://pubs.kist.re.kr/handle/201004/113109 This paper proposes a method of estimating the pose of a mobile robot by using a learning model. When estimating the pose of a mobile robot, wheel encoder and inertial measurement unit (IMU) data are generally utilized. However, depending on the condition of the ground surface, slip occurs due to interaction between the wheel and the floor. In this case, it is hard to predict pose accurately by using only encoder and IMU. Thus, in order to reduce pose error even in such conditions, this paper introduces a pose estimation method based on a learning model using data of the wheel encoder and IMU. As the learning model, long short-term memory (LSTM) network is adopted. The inputs to LSTM are velocity and acceleration data from the wheel encoder and IMU. Outputs from network are corrected linear and angular velocity. Estimated pose is calculated through numerically integrating output velocities. Dataset used as ground truth of learning model is collected in various ground conditions. Experimental results demonstrate that proposed learning model has higher accuracy of pose estimation than extended Kalman filter (EKF) and other learning models using the same data under various ground conditions. Korean 한국로봇학회 모바일 로봇을 위한 학습 기반 관성-바퀴 오도메트리 Learning-based Inertial-wheel Odometry for a Mobile Robot Article 10.7746/jkros.2023.18.4.427 2 로봇학회 논문지, v.18, no.4, pp.427 - 435 로봇학회 논문지 18 4 427 435 N kci ART003018233 IMU Learning Mobile Robot Odometry Wheel