Registration for 3-D LiDAR Datasets Using Pyramid Reference Object

Accurate observation and comprehension of the surroundings are made possible by 3-D reconstruction technology. This study suggests a pyramid reference object for a 3-D environmental reconstruction system. First, LiDAR sensors are used to scan the indoor scene vertically in all directions. Then, the random sample consensus (RANSAC) algorithm and the three-axis least-squares method (3A-LSM) 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 iterative closest point (ICP) 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 3-D reconstruction system. By using the loop closure optimization process, the LiDAR self-localization error is decreased by 29.4%, and the global environmental reconstruction precision is increased by 12%.