基于图优化方法的点云三维环境重构

Three-dimensional Reconstruction of Point Cloud Based on Graph Optimization Method

点云的三维重构对无人车感知和高精度地图的制作具有重要作用。为得到与真实环境一致的点云三维环境,提出以GPS差分状态良好的点云为参考,将闭环图优化延伸到非闭环条件下,分别对局部差分不好的路段进行ICP配准和图优化,在此基础上建立全局图结构实施精细优化的算法。通过实验发现,运用该算法消除了配准的累积误差和轨迹重合区域的点云误差,得到全局相对位置一致的点云,达到与真实环境一致的三维重构效果。

Three-dimensional reconstruction of point cloud plays an important role in perceiving unmanned vehicles and making high precision maps. In order to obtain three-dimensional environment of point cloud which is consistent with real environment,the paper firstly proposes a method that extending the closed-loop graph to non-closed-loop graph with point cloud of good GPS differential state as reference. Then,it conducts ICP registration and graph optimization on partial bad differential section respectively,and establishes global graph structure to implement fine optimization. The experiment shows that this algorithm eliminates cumulative error of the registration and point cloud error in the intersection area,and it obtains the point cloud which has relative position in global and achieves three-dimensional reconstruction effect consistent with real environment.