지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록· 키워드
오류제보하기
Recently, with the emergence of autonomous vehicles and the increasing interest in safety, a variety of research has been being actively conducted to precisely estimate the position of a vehicle by fusing sensors. Previously, researches were conducted to determine the location of moving objects using GNSS (Global Navigation Satellite Systems) and/or IMU (Inertial Measurement Unit). However, precise positioning of a moving vehicle has lately been performed by fusing data obtained from various sensors, such as LiDAR (Light Detection and Ranging), on-board vehicle sensors, and cameras. This study is designed to enhance kinematic vehicle positioning performance by using feature-based recognition. Therefore, an analysis of the required precision of the observations obtained from the images has carried out in this study. Velocity and attitude observations, which are assumed to be obtained from images, were generated by simulation. Various magnitudes of errors were added to the generated velocities and attitudes. By applying these observations to the positioning algorithm, the effects of the additional velocity and attitude information on positioning accuracy in GNSS signal blockages were analyzed based on Kalman filter. The results have shown that yaw information with a precision smaller than 0.5 degrees should be used to improve existing positioning algorithms by more than 10%.
목차
Abstract
1. Introduction
2. Vehicle Positioning Algorithm
3. Hardware Platform and Study Area
4. Results and Analysis
5. Summary and Conclusion
References
참고문헌 (8)
참고문헌 신청
Grimes, M. and LeCun, Y. (2009), Efficient off-road localization using visually corrected odometry. IEEE International Conference on Robotics and Automation, 12- 17 May, Kobe, Japan, pp. 2649-2654.
Han, J.H. (2016), Performance Analysis of Positioning through the Integration of GNSS and On-board Vehicle Sensors, Ph.D. dissertation, University of Seoul, Seoul, Korea, 162p.
Jung, J., Kwon, J.H., and Lee, Y. (2017), Development of Image-based Assistant Algorithm for Vehicle Positioning by Detecting Road Facilities. Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, Vol. 35, No. 5, pp. 339-347.
Lee, Y. and Kwon J.H. (2018), Performance analysis of the GNSS/MEMS-IMU/on-Board vehicle sensor/ magnetometer-based positioning system during GNSS signal blockage, International Journal of Urban Sciences , Vol. 23, No. 3, pp. 1-10.
Li, T., Zhang, H., Gao, Z., Niu, X., and El-Sheimy, N. (2019), Tight Fusion of a Monocular Camera, MEMS-IMU, and Single-Frequency Multi-GNSS RTK for Precise Navigation in GNSS-Challenged Environments. Remote Sensing, Vol. 11, No. 6, pp. 1-24.
Han, J.H. (2016), Performance Analysis of Positioning through the Integration of GNSS and On-board Vehicle Sensors, Ph.D. dissertation, University of Seoul, Seoul, Korea, 162p.
Jung, J., Kwon, J.H., and Lee, Y. (2017), Development of Image-based Assistant Algorithm for Vehicle Positioning by Detecting Road Facilities. Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, Vol. 35, No. 5, pp. 339-347.
Lee, Y. and Kwon J.H. (2018), Performance analysis of the GNSS/MEMS-IMU/on-Board vehicle sensor/ magnetometer-based positioning system during GNSS signal blockage, International Journal of Urban Sciences , Vol. 23, No. 3, pp. 1-10.
Li, T., Zhang, H., Gao, Z., Niu, X., and El-Sheimy, N. (2019), Tight Fusion of a Monocular Camera, MEMS-IMU, and Single-Frequency Multi-GNSS RTK for Precise Navigation in GNSS-Challenged Environments. Remote Sensing, Vol. 11, No. 6, pp. 1-24.
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UCI(KEPA) : I410-ECN-0101-2019-533-001289020