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- W4385453072 abstract "Accurate observation and comprehension of the surroundings are made possible by 3D reconstruction technology. This study suggests a pyramid reference object for a 3D environment reconstruction system. First, LiDAR sensors are used to scan the indoor scene vertically in all directions. Then, the RANSAC(Random Sample Consensus) algorithm and the 3A-LSM( three-axis least square method) are used to precisely determine the plane equation of the calibration object in the LiDAR point clouds. The virtual feature corners are identified as the spots where the reference planes connect at a defined distance. The ICP (Iterative Closest Point) algorithm is used to predict the spatial transformation matrices of the LiDAR sensor between the successive frames based on the retrieved virtual corners. The loop closure optimization module, which optimizes the virtual corner extraction process to reduce the accumulated error in global mapping, is also added to the 3D reconstruction system. By using the loop closure optimization process, the LiDAR self-localization error is decreased by 29.4%, and the global environment reconstruction precision is increased by 12%." @default.
- W4385453072 created "2023-08-02" @default.
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- W4385453072 date "2023-01-01" @default.
- W4385453072 modified "2023-10-16" @default.
- W4385453072 title "Registration for 3D LiDAR Datasets using Pyramid Reference Object" @default.
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- W4385453072 doi "https://doi.org/10.1109/tim.2023.3300410" @default.
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