Abstract
The emergence of low-cost micro-electro-mechanical system inertial measurement units, cameras and global navigation satellite system (GNSS) receivers has promoted the research of multisensor fusion positioning. With the rapid development of chip technology, it has become a trend for low-cost GNSS chips to provide multi-frequency carrier phase observations while supporting multiple constellations. To enhance the positioning performance of multi-frequency and multi-system precise point positioning (PPP) in the difficult urban environment, we propose a tightly coupled system of monocular visual-inertial odometry (MVIO) and uncombined PPP (UC-PPP) based on factor graph optimization. The initialization of MVIO and UC-PPP adopts a coarse-to-fine approach to correct the transformation of local and global frames online. Moreover, the sliding window and marginalization methods are adopted to retain the constraints between adjacent observations and eliminate useless observations in the window. The pedestrian and vehicle tests in urban environments verify the performance of the proposed method. Compared with open-source software GVINS, the positioning accuracy of the proposed method has been further improved by using carrier phase observations with higher measurement accuracy. Compared with PPP alone, the improvement of the proposed method for the low-speed and short-distance pedestrian test in the east, north, and up directions is 73.3, 54.8 and 62.7%, respectively, while the improvement for the high-speed and long-distance vehicle test is 63.0, 59.3 and 70.5%, respectively. Experiment results show that the proposed method has better positioning accuracy and continuity in difficult urban environments.
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Data availability
The datasets used in the pedestrian and vehicle tests are available at https://github.com/HKUST-Aerial-Robotics/GVINS-Dataset.
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Acknowledgements
Thanks to the HKUST Aerial Robotics Group for the open datasets. Cheng Pan would like to thank the China Scholarship Council for sponsoring his visit to ETH Zurich.
Funding
This work was supported by the National Natural Science Foundation of China (41974026), the Graduate Innovation Program of China University of Mining and Technology (2022WLKXJ108) and the Postgraduate Research and Practice Innovation Program of Jiangsu Province (KYCX22_2591).
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CP: Conceptualization, methodology, software, writing—original draft. FL, YP, YW: Investigation, writing—review and editing. BS and JG: Supervision, writing—review and editing. ZL: Supervision, Project administration, Funding acquisition. All authors read and approved the final manuscript.
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Pan, C., Li, F., Pan, Y. et al. Tightly coupled integration of monocular visual-inertial odometry and UC-PPP based on factor graph optimization in difficult urban environments. GPS Solut 28, 45 (2024). https://doi.org/10.1007/s10291-023-01586-3
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DOI: https://doi.org/10.1007/s10291-023-01586-3