The efficiency and accuracy of point set registration is always a challenge to deal with. In this paper, we propose an improved algorithm using a scale invariant feature extracted. The larger transformation scale and rotation angle cause lower registration accuracy. The initial corresponding point set can be obtained using a scale invariant feature transform (SIFT) operator. In addition, the geometric features of the points are combined to remove the wrong points. The unit quaternion algorithm is used to estimate the best rigid body transformation matrix for precise registration. The experiments show that the registration accuracy increases by 7.99%, while the time consumption decreased by 6.24% in a typical indoor scene.

点集注册的效率和准确性始终是一个挑战。在本文中，我们提出了一种使用尺度不变特征提取的改进算法。较大的变换比例和旋转角度会导致较低的套准精度。初始对应点集可以使用尺度不变特征变换（SIFT）运算符获得。另外，点的几何特征被组合以去除错误的点。单元四元数算法用于估计最佳刚体变换矩阵以进行精确配准。实验表明，在典型的室内场景中，配准精度提高了7.99％，而时间消耗却减少了6.24％。