面向深度相机位姿优化的帧间点云数据配准算法

TOF相机能够同时采集灰度图像和深度图像从而优化相机位姿的估计值. 应用图结构调整框架优化多帧数据采集时的相机位姿，采用帧间配准决定优化的精度和效率. 从2帧图像上提取并匹配尺度不变特征点对，二维特征点被扩展到三维空间后，利用与特征点的空间位置关系将2帧三维点云配准；逐步应用提出的算法配准参与位姿优化的多帧点云中的任意2帧点云；最后将有效配准的点云帧对作为输入数据，采用图结构算法优化位姿. 实验结果表明，提出的帧间配准算法使得位姿估计值精度显著提高，同时保证了估计效率.

Inter frame point clouds registration algorithm for pose optimization of depth camera

TOF (time of flight) camera can collect gray and depth images simultaneously to optimize the estimation of camera pose. Graph-based adjustment structure was applied to optimize the poses of the TOF camera in acquiring several frames. Registration between frames is a key operation which determines both the efficiency and effectiveness of the camera pose optimization. Scale invariant features were detected from a pair of images and matched subsequently. After the 2D feature points were extended into 3D space, two point clouds were registered in terms of relative positions between the features and the normal 3D points. Among all of the point clouds participating in the optimization of camera pose, any two point clouds were registered pair by pair using the proposed registering method. Lastly, the graph based algorithm was employed to adjust the camera poses, with inputs of the valid pairs of registered point clouds. Results demonstrated that the proposed method can improve the precision of the optimized camera pose, and the estimating efficiency is guaranteed.