Abstract: | The global position precision of High-definition (HD) Map mainly relies on GNSS Real-Time Kinematic (RTK) positioning. RTK solution cannot be fixed in long urban canyon or long tree-lined roads for the non-line-of sight (NLOS) of signal and multi-path effect of signal reflection, consequently, the localization error in relative localization system such as IMU/wheel encoder, visual-inertial odometry (VIO), Lidar-inertial odometry (LIO) will drift dramatically. To improve the large drift error under long range GNSS denied environment, a multi-sensor-fusion localization algorithm assisted by pole-like objects registration is proposed in this paper. Firstly, an efficient relative pose observation model for two distant states are built by registration of pole-like object is proposed, in which an effective registration algorithm between pole-like objects detected by point-cloud and that detected by semantic segmentation on image plane algorithm is proposed. Secondly, a multi-sensor-fusion localization system which incorporate pole-like objects registration is proposed to get more precise global positioning result. The evaluation of the method proposed in this paper was carried out by the data acquired from a HD Map acquisition vehicle, the result shows that relative positioning precision improved from 0.5% distance to 0.18% distance error, the max absolute positioning error improved from 0.42 meter to 0.18 meter whenever the RTK is unavailable in a tracking distance of 500 meters. |
Published in: |
Proceedings of the 35th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2022) September 19 - 23, 2022 Hyatt Regency Denver Denver, Colorado |
Pages: | 1989 - 1998 |
Cite this article: | Ma, Yanhai, Wang, Shuai, Wang, Xudong, Wang, Yongliang, "Multi-Sensor-Fusion Localization Assisted by PoleLike Objects in Long Range GNSS Degraded Environment," Proceedings of the 35th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2022), Denver, Colorado, September 2022, pp. 1989-1998. https://doi.org/10.33012/2022.18548 |
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