Shi, Y., Lian, B., Zeng, Y., Kurniawan, E., & Ma, Y. (2024). GVIL: Tightly Coupled GNSS-Visual-Inertial-Lidar for Position Estimation in Challenging Environments. 2024 IEEE 99th Vehicular Technology Conference (VTC2024-Spring), 1–5. https://doi.org/10.1109/vtc2024-spring62846.2024.10683379
Abstract:
The fusion framework based on Light Detection and Ranging (LiDAR) and vision is susceptible to environmental constraints. Therefore, they are unable to adapt to complex and challenging environments. In response to this, this paper proposes a tightly coupled framework based on Global Navigation Satellite System (GNSS) data, which tightly couples GNSS, inertial, LiDAR and visual data. The experimental results demonstrate that, using publicly available challenging environment datasets, the proposed fusion system can achieve a 52.68% improvement in positioning accuracy compared to some existing fusion algorithms.
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Funding Info:
This research / project is supported by the NRF - FCP
Grant Reference no. : FCP-NTURG- 2022-021, FCP-ASTAR-TG-2022-003
This research / project is supported by the National Natural Science Foundation of China - Grant
Grant Reference no. : 62173276, 62171735, 62101458, 62001392, 61803310, 61801394
This research / project is supported by the Natural Science Basic Research Program of Shaanxi - Grant
Grant Reference no. : 2022GY-097,2021JQ-122, 2021JQ-693
This research / project is supported by the China Postdoctoral Science Foundation - Grant
Grant Reference no. : 2020M673482, 2020M673485