Abstract
Accurate positioning algorithms are crucial for autonomous vehicle navigation and robotics. The fusion of data from GNSS, INS, and odometers can provide comprehensive positioning results across various environments. However, effectively integrating data from sources with varying reliability levels remains a significant challenge. To address this challenge, we propose a fusion positioning framework that dynamically optimizes the weights of navigation sources. This framework leverages a plug-and-play factor graph algorithm and utilizes a padding mask to flexibly extract features from opportunistically acquired sensor data. It learns the relative fusion weights of different navigation systems based on these data features, thus offering more robust and accurate positioning results in complex and dynamic urban environments. Comprehensive experiments and evaluations demonstrate the effectiveness and superiority of our algorithm.
F. Wu and Z. Zhou—Equal contribution.
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Acknowledgements
This work was supported in part by the Strategic Priority Research Program of Chinese Academy of Sciences under Grant XDA28040500, the National Natural Science Foundation of China under Grant 62261042, the Key Research Projects of the Joint Research Fund for Beijing Natural Science Foundation and the Fengtai Rail Transit Frontier Research Joint Fund under Grant L221003, the Beijing Natural Science Foundation under Grant 4232035 and 4222034, and the BUPT Excellent Ph.D. Students Foundation CX2022131.
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Wu, F., Zhou, Z., Luo, H., Zhao, F., Zhou, B. (2025). CAFGO: Confidence-Adaptive Factor Graph Optimization Algorithm for Fusion Localization. In: Hadfi, R., Anthony, P., Sharma, A., Ito, T., Bai, Q. (eds) PRICAI 2024: Trends in Artificial Intelligence. PRICAI 2024. Lecture Notes in Computer Science(), vol 15281. Springer, Singapore. https://doi.org/10.1007/978-981-96-0116-5_28
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DOI: https://doi.org/10.1007/978-981-96-0116-5_28
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