智慧城市建设亟需高质量的点云数据,移动测量点云的全局一致性优化是影响点云质量的重要方面,SLAM(simultaneous localization and mapping)技术由于其时序匹配的性质难以提供全局层面的约束,而高精度惯导成本较高,不利于普及.如何基于低成本惯导获取高质量点云成为当下面临的挑战.为对全局点云一致性进行优化,本文对点云地图进行子图分割,然后根据子图建立子图与子图之间回环约束,并构建基于回环、IMU(inertial measurement unit)预积分、GNSS差分和激光里程计四种约束的平差方程,实现移动测量点云的全局优化以及轨迹的全局改正.同时本文采用头盔式移动测量系统采集的多源数据对本文算法进行实验验证,通过对比分析,验证了本方法的有效性以及应用潜力.
An Optimization Method of Helmet-Based Point Cloud's Loop Constrained Global Consistency
The construction of smart cities demands high-quali-ty point cloud data. On the one hand,SLAM technologies register the data based on time series,it is hard to optimize the consistency of point cloud globally,on the other hand,putting on a high-end IMU is sometimes difficult due to the high cost. Obtaining high-quality point clouds on low-cost IMUs is a key challenge. To improve the point cloud quality,this paper proposes a method by segmenting sub-maps,build-ing loop constraints,and then solving the equations with point cloud matching constraints,IMU pre-integration constraints,GNSS PPK constraints,and LiDAR odometry constraints. This method optimizes point cloud and corrects the trajectory globally. Meanwhile,the multi-source data from a helmet-based mobile mapping system is used to verify the approach. The comparison and analysis results demonstrate the effective-ness of this method and its upcoming potential for wider appli-cations.
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