一种鲁棒的城市复杂动态场景点云配准方法

针对城市道路环境中面临的动态目标繁多、遮挡严重、以及GPS （全球定位系统）误差较大的问题，提出了一种无需检测动态目标且可以适应不同初始位置误差的快速、鲁棒的配准方法.首先，使用区域生长方法对去除地面的障碍物点云进行目标分割，并通过设定约束条件优化分割效果，生成点云的目标重心点集合；然后，提出了一种多层嵌套的RANSAC （随机抽样一致性）算法架构，迭代地更新配准结果，实现重心点集的粗配准并去除外点；最后，利用ICP （迭代最近点）进行点云的精确配准.与传统RANSAC方法的对比实验表明，该方法能够在复杂的动态场景和较大的初始位置误差下完成精确可靠的点云配准，且其配准成功率和配准速度明显高于传统方法.

A Robust Point Cloud Registration Method in Urban Dynamic Environment

Considering the enormous moving objects, serious occlusions, and low precision of GPS (global positioning system) in urban dynamic environment, a fast and robust registration method which can adapt to different initial position errors without detecting dynamic objects is proposed. Firstly, the region growing method is used for object segmentation of obstacle point cloud without ground data; and by setting constraint conditions, segmentation result is optimized to generate object gravity centers of one point cloud. Then, an algorithm framework with multi-nested loop of RANSAC (random sample consensus) in which registration results is updated iteratively is put forward to achieve rough registration of centroid sets and remove outliers. ICP (iterative closest point) is finally used for precise registration. The contrast experiments with the traditional RANSAC algorithm show that the proposed method can achieve accurate and reliable point cloud registration in complex dynamic scenes with large initial position errors, and the registration success rate and registration speed are significantly higher than those of traditional methods.