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.

Collaborative robots have promising potential for widespread use in small-and-medium-sized enterprise (SME) manufacturing and production due to the development of increasingly sophisticated Human-Robot Collaboration technologies. However, predicting and identifying the behavior of collaborative robots remains a challenging problem due to the significant non-linear properties of their unique gearbox, the harmonic drive. To tackle the engineering problem, this work proposes a physics-informed neural network (PINN) to predict and identify collaborative robot joint dynamics. The procedure involves deriving the state-space dynamic model, embedding the system's dynamics into a recurrent neural network (RNN) with customized Runge-Kutta cells, obtaining labeled training data, predicting system responses, and estimating dynamic parameters. The proposed method is applied to predict and identify collaborative robot joint dynamics, and the results are verified and validated through numerical simulations and experimental testing, respectively. The obtained results demonstrate a high level of agreement with the ground truth and exhibit superior performance compared to the conventional PINN and the non-linear grey-box state-space estimation algorithm when confronted with non-linearity and dynamic coupling. Moreover, the PINN exhibits the potential for extension to various dynamic systems.

Hand-eye calibration, a fundamental task in vision-based robotic systems, is commonly equipped with collaborative robots, especially for robotic applications in small and medium-sized enterprises (SMEs). Most approaches to hand-eye calibration rely on external markers or human assistance. We proposed a novel methodology that addresses the hand-eye calibration problem using the robot base as a reference, eliminating the need for external calibration objects or human intervention. Using point clouds of the robot base, a transformation matrix from the coordinate frame of the camera to the robot base is established as “I=AXB.” To this end, we exploit learning-based 3D detection and registration algorithms to estimate the location and orientation of the robot base. The robustness and accuracy of the method are quantified by ground-truth-based evaluation, and the accuracy result is compared with other 3D vision-based calibration methods. To assess the feasibility of our methodology, we carried out experiments utilizing a low-cost structured light scanner across varying joint configurations and groups of experiments. The proposed hand-eye calibration method achieved a translation deviation of 0.930 mm and a rotation deviation of 0.265 degrees according to the experimental results. Additionally, the 3D reconstruction experiments demonstrated a rotation error of 0.994 degrees and a position error of 1.697 mm. Moreover, our method offers the potential to be completed in 1 second, which is the fastest compared to other 3D hand-eye calibration methods. We conduct indoor 3D reconstruction and robotic grasping experiments based on our hand-eye calibration method. Related code is released athttps://github.com/leihui6/LRBO.