A point cloud as a collection of points is poised to bring about a revolution in acquiring and generating three-dimensional (3D) surface information of an object in 3D reconstruction, industrial inspection, and robotic manipulation. In this revolution, the most challenging but imperative process is point could registration, i.e., obtaining a spatial transformation that aligns and matches two point clouds acquired in two different coordinates. In this survey paper, we present the overview and basic principles, give systematical classification and comparison of various methods, and address existing technical problems in point cloud registration. This review attempts to serve as a tutorial to academic researchers and engineers outside this field and to promote discussion of a unified vision of point cloud registration. The goal is to help readers quickly get into the problems of their interests related to point could registration and to provide them with insights and guidance in finding out appropriate strategies and solutions.

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Many collaborative robots use strain-wave-type transmissions due to their desirable characteristics of high torque capacity and low weight. However, their inherent complex and nonlinear behavior introduces significant errors and uncertainties in the robot dynamics calibration, resulting in decreased performance for motion and force control tasks and lead-through programming applications. This paper presents a new method for calibrating the dynamic model of collaborative robots. The method combines the known inverse dynamics identification model with the weighted least squares (IDIM-WLS) method for rigid robot dynamics with complex nonlinear expressions for the rotor-side dynamics to obtain increased calibration accuracy by reducing the modeling errors. The method relies on two angular position measurements per robot joint, one at each side of the strain-wave transmission, to effectively compensate the rotor inertial torques and nonlinear dynamic friction that were identified in our previous works. The calibrated dynamic model is cross-validated and its accuracy is compared to a model with parameters obtained from a CAD model. Relative improvements are in the range of 16.5% to 28.5% depending on the trajectory.

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