Abstract
Nowadays, a precise and robust localization system is essential for an autonomous vehicle to operate safely and effectively. Currently, individual sensor still can not reach a satisfying level in both accuracy and robustness. It is a feasible solution to build a localization system by fusing multi-type sensors and incorporating map information. This paper proposes a real-time multi-sensor fusion method based on the particle filter framework. It utilizes the onboard IMU, camera, lidar, and digital map information. The main novelty and contribution of this paper lie in combining visual and distance measurements into semantic features to correct the states. From a mass production perspective, the proposed system is satisfactory in terms of accuracy and map volume. The proposed method is quantitatively evaluated in real complex scenarios. The experimental results show the effectiveness and accuracy of the proposed system.
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© 2023 Beijing HIWING Sci. and Tech. Info Inst
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Zhang, Z., Huang, C., Li, L. (2023). Vehicle Localization on Semantic Map by Combining Visual and Distance Measurements. In: Fu, W., Gu, M., Niu, Y. (eds) Proceedings of 2022 International Conference on Autonomous Unmanned Systems (ICAUS 2022). ICAUS 2022. Lecture Notes in Electrical Engineering, vol 1010. Springer, Singapore. https://doi.org/10.1007/978-981-99-0479-2_146
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DOI: https://doi.org/10.1007/978-981-99-0479-2_146
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