A fast and accurate algorithm is presented to register scans from an RGB-D camera, which rotates and scans the entire scene in an automated fashion on a pan-tilt platform. The proposed algorithm, Axis Bound Registration, exploits the movement of the camera that is bound by the two rotation axes of pan-tilt servos, so as to realize fast and accurate registration of acquired point clouds. The rotation parameters, including the rotation axes, pan-tilt transformations and the servo control mechanism, are calibrated beforehand. Subsequently, fast global registration can be performed during online operation with transformation matrices formed by the calibrated rotation axes and angles. In local registration, features are extracted and matched between two scenes. For robust registration, false-positive correspondences are rejected based on the distances of pre-oriented keypoint pairs, namely the circle of deviation constraint. Then, a more accurate registration can be achieved by minimizing the residual distances between correspondence pairs, while estimated transformations are bound to the rotation axes. Results of comparative experiments validate that the proposed method outperforms state-of-the-art algorithms of various approaches based on camera calibration, global registration, and simultaneous-localization-and-mapping in terms of root-mean-square error and computation time.

提出了一种快速，准确的算法来记录来自RGB-D摄像机的扫描，该摄像机在旋转云台上以自动方式旋转和扫描整个场景。提出的算法，轴绑定配准，利用受云台伺服器两个旋转轴限制的摄像机运动，以实现快速准确地捕获点云的配准。包括旋转轴，旋转云台变换和伺服控制机构在内的旋转参数均已预先校准。随后，可以在在线操作期间使用由校准的旋转轴和角度形成的变换矩阵执行快速全局配准。在本地注册中，提取特征并在两个场景之间进行匹配。为了进行鲁棒的配准，将基于预先确定的关键点对的距离（即偏差圆）拒绝假阳性对应关系约束。然后，通过将对应对之间的剩余距离最小化，可以实现更准确的配准，同时将估计的变换绑定到旋转轴。比较实验的结果证明，该方法在基于均方根误差和计算时间方面优于基于摄像机校准，全局配准和同时定位和映射的各种方法的最新算法。