激光雷达场景三维姿态点法向量估计方法

激光成像雷达能够获取反映目标三维空间位置的点云数据，可直接估计目标三维姿态角，是完成特征提取、目标配准等工作的重要参数。实现场景的三维姿态估计，借鉴基于点法向量的三维姿态估计算法（PDVA），针对真实场景中表征场景坐标系（SCS）坐标轴的正方向向量偏差较大的问题，提出了一种优化的三维姿态估计算法（OPDVA）。该方法利用场景点云存在大面积近似平面区域的特点，通过随机抽样一致算法（RANdom SAmple Consensus，RANSAC）的平面模型对聚类中其他方向的点法向量进行滤除，得到最优拟合平面对应的法向量即为修正后的SCS坐标轴。利用旋转变换和重采样等技术手段，分别采用矩形包围盒法、PDVA和OPDVA对3组真实场景距离像进行实验。实验结果表明:OPDVA方法对场景的姿态估计明显优于其他两种方法，姿态估计误差不超过4°，对存在遮挡的场景也同样适用。

Pose estimation algorithms for lidar scene based on point normal vector

Laser imaging radar can obtain point cloud data reflecting the three-dimensional position of the target, directly estimate the three-dimensional attitude angle of the target, and is an important parameter for feature extraction and target registration. To realize the three-dimensional attitude estimation of scenes, an optimized three-dimensional attitude estimation algorithm(OPDVA) based on point normal vector (PDVA) was proposed to solve the problem of large deviation of the positive vector representing the coordinate axis of scene coordinate system(SCS) in real scenes. In this method, remove point normal vectors in other directions in the cluster by RANdom SAmple Consensus (RANSAC) plane model was removed, and the corresponding normal vectors of the optimal fitting plane were the revised SCS coordinate axes. Using rotational transformation and resampling techniques, 3 groups of real scene range image were experimented with rectangular bounding box method, PDVA and OPDVA respectively. The experimental results show that the OPDVA method is superior to the other two methods in pose estimation. The error of pose estimation does not exceed 4°, and it is also suitable for occlusion scenarios.