모바일 로봇을 위한 학습 기반 관성-바퀴 오도메트리
Learning-based Inertial-wheel Odometry for a Mobile Robot
0
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
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.
Abstract
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.
참고문헌 (27)
- J. Borenstein, "Measurement and correction of systematic odometry errors in mobile robots", IEEE Transactions on Robotics and Automation, 12(6), pp.869~880 (1996), Institute of Electrical and Electronics Engineers (IEEE)[DOI]
- Kok Seng Chong, "Accurate odometry and error modelling for a mobile robot", Proceedings of International Conference on Robotics and Automation, 4, pp.2783~2788 (1997), IEEE
- Agostino Martinelli, "Simultaneous localization and odometry self calibration for mobile robot", Autonomous Robots, 22(1), pp.75~85 (2006), Springer Science and Business Media LLC[DOI]
- A. Angelova, "Slip Prediction Using Visual Information", Robotics: Science and Systems II (2006), Robotics: Science and Systems Foundation[DOI]
- Daniel M. Helmick, "Slip-compensated path following for planetary exploration rovers", Advanced Robotics, 20(11), pp.1257~1280 (2006), Informa UK Limited[DOI]
- Tong Qin, "VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator", IEEE Transactions on Robotics, 34(4), pp.1004~1020 (2018), Institute of Electrical and Electronics Engineers (IEEE)[DOI]
- Tong Qin, "Online Temporal Calibration for Monocular Visual-Inertial Systems", 2018 IEEE, 2018, 3662~3669[DOI]
- Ji Zhang, "LOAM: Lidar Odometry and Mapping in Real-time", Robotics: Science and Systems X (2014), Robotics: Science and Systems Foundation[DOI]
- Tixiao Shan, "LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping", 2020 IEEE, 2020, 5135~5142[DOI]
- Shibo Zhao, "Super Odometry: IMU-centric LiDAR-Visual-Inertial Estimator for Challenging Environments", 2021 IEEE, 2021, ~[DOI]
- Jingang Yi, "Kinematic Modeling and Analysis of Skid-Steered Mobile Robots With Applications to Low-Cost Inertial-Measurement-Unit-Based Motion Estimation", IEEE Transactions on Robotics, 25(5), pp.1087~1097 (2009), Institute of Electrical and Electronics Engineers (IEEE)[DOI]
- Uche Onyekpe, "WhONet: Wheel Odometry neural Network for vehicular localisation in GNSS-deprived environments", Engineering Applications of Artificial Intelligence, 105, pp.104421~ (2021), Elsevier BV[DOI]
- Martin Brossard, "AI-IMU Dead-Reckoning", IEEE Transactions on Intelligent Vehicles, 5(4), pp.585~595 (2020), Institute of Electrical and Electronics Engineers (IEEE)[DOI]
- Mahdi Abolfazli Esfahani, "OriNet: Robust 3-D Orientation Estimation With a Single Particular IMU", IEEE Robotics and Automation Letters, 5(2), pp.399~406 (2020), Institute of Electrical and Electronics Engineers (IEEE)[DOI]
- Martin Brossard, "Denoising IMU Gyroscopes with Deep Learning for Open-Loop Attitude Estimation", IEEE Robotics and Automation Letters, pp.1~1 (2020), Institute of Electrical and Electronics Engineers (IEEE)[DOI]
- Sachini Herath, "RoNIN: Robust Neural Inertial Navigation in the Wild: Benchmark, Evaluations, & New Methods", 2020 IEEE International Conference on Robotics and Automation (ICRA), pp.3146~3152 (2020), IEEE
- Wenxin Liu, "TLIO: Tight Learned Inertial Odometry", IEEE Robotics and Automation Letters, 5(4), pp.5653~5660 (2020), Institute of Electrical and Electronics Engineers (IEEE)[DOI]
- Ming Zhang, "IMU Data Processing For Inertial Aided Navigation: A Recurrent Neural Network Based Approach", 2021 IEEE International Conference on Robotics and Automation (ICRA), pp.3992~3998 (2021), IEEE
- Martin BROSSARD, "Learning Wheel Odometry and IMU Errors for Localization", 2019 International Conference on Robotics and Automation (ICRA), pp.291~297 (2019), IEEE
- A. Geiger, "Are we ready for autonomous driving? The KITTI vision benchmark suite", 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp.3354~3361 (2012), IEEE
- Jinyong Jeong, "Complex urban dataset with multi-level sensors from highly diverse urban environments", The International Journal of Robotics Research, 38(6), pp.642~657 (2019), SAGE Publications[DOI]
- Nicholas Carlevaris-Bianco, "University of Michigan North Campus long-term vision and lidar dataset", The International Journal of Robotics Research, 35(9), pp.1023~1035 (2015), SAGE Publications[DOI]
- Michael Burri, "The EuRoC micro aerial vehicle datasets", The International Journal of Robotics Research, 35(10), pp.1157~1163 (2016), SAGE Publications[DOI]
- David Schubert, "The TUM VI Benchmark for Evaluating Visual-Inertial Odometry", 2018 IEEE, 2018, 1680~1687[DOI]
- A. Paszke, "PyTorch: An imperative style, high-performance deep learning library", Advances in neural information processing systems, 32 (2019)
- D. P. Kingma, "Adam : A method f or s tochastic optimization", ~ (2014)
- Valentin Peretroukhin, "DPC-Net: Deep Pose Correction for Visual Localization", IEEE Robotics and Automation Letters, 3(3), pp.2424~2431 (2018), Institute of Electrical and Electronics Engineers (IEEE)[DOI]