A benchmark survey of rigid 3D point cloud registration algorithms
Faculty of Sciences. Mathematics and Computer Science
Faculty of Applied Engineering Sciences
Engineering sciences. Technology
International Journal on Advances in Intelligent Systems
, p. 118-127
University of Antwerp
Advanced user interface sensors are able to observe the environment in three dimensions with the use of specific optical techniques such as time-of-flight, structured light or stereo vision. Due to the success of modern sensors, which are able to fuse depth and color information of the environment, a new focus on different domains appears. This survey studies different state- of-the-art registration algorithms, which are able to determine the motion between two corresponding 3D point clouds. This survey starts from a mathematical field of view by explaining two deterministic methods, namely Principle Component Analysis (PCA) and Singular Value Decomposition (SVD), towards more iteratively methods such as Iterative Closest Point (ICP) and its variants. We compare the performance of the different algorithms to their precision and robustness based on a real world dataset. The main contribution of this survey consists of the performance benchmark that is based on a real world dataset, which includes 3D point clouds of a Microsoft Kinect camera, and a mathematical overview of different registration methods, which are commonly used in applications such as simultaneous localization and mapping, and 3D-scanning. The outcome of our benchmark concludes that the ICP point-to-surface method is the most precise algorithm. Beside the precision, the result for the robustness we can conclude that a combination of applying a ICP point-to-point method after an SVD method gives the minimum error.