Automatic registration of point clouds is a challenging task especially when the overlap between them is too small to initialize the traditional iterative closest point algorithm directly. A method for registering 3D point clouds in different coordinates was proposed by using the scanner’s pose information, recorded by a robot. This method consisted of two steps: firstly, 3D scanner was set on the end effector of the robot, which recorded the 6D pose of the scanner when an object was scanned in real time. Using this recorded pose information, the captured point clouds from different scanner coordinates were transformed to robot base coordinate. Secondly, the weighted sparse iterative closest point was used to align the point clouds in robot base coordinate which refines the result of the first step. This method was tested on various data and situations.The experiment results show that the proposed method could align point clouds with lower overlapping ratio, and is more accurate, faster and more robust to outliers than existing methods.