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3D mapping provides highly accurate environmental data, which is essential for critical applications such as autonomous driving and urban emergency response. Light detection and ranging (LiDAR) sensors, particularly solid-state ones, play a pivotal role in spatial–temporal mapping by providing precise three-dimensional data of the environment, significantly enhancing remote sensing capabilities and adaptability to challenging environments compared to mechanical LiDAR systems. However, the limited field of view results in a sparse point cloud frame with few features, which poses challenges to feature matching, causes pose offset, and hinders spatial–temporal continuity, and further significant obstacle for existing vehicle-mounted mobile mapping methods. To address the above issues, we proposed a novel approach that integrating inertial measurement unit (IMU) with solid-state LiDAR. Specifically, it comprises two key modules: an initial localization mapping module, mitigating the limitations of solid-state LiDAR in positioning and mapping accuracy, and an attitude optimization mapping module utilizing real-time high-frequency IMU data to identify key frames for correcting initial attitudes and generating accurate 3D maps. The effectiveness of the method is validated through extensive experiments in complex community and high-speed urban road scenarios. Furthermore, our approach outperforms than the state-of-the-art techniques in test scenarios, achieving a significant 35% reduction in average absolute pose error and enhancing the robustness of vehicle-mounted mapping. |
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