Sensors producing 3D point clouds such as 3D laser scanners and RGB-D cameras are widely used in robotics, be it for autonomous driving or manipulation. Aligning point clouds produced by these sensors is a vital component in such applications to perform tasks such as model registration, pose estimation, and SLAM. Iterative closest point (ICP) is the most widely used method for this task, due to its simplicity and efficiency. In this paper we propose a novel method which solves the optimisation problem posed by ICP using stochastic gradient descent (SGD). Using SGD allows us to improve the convergence speed of ICP without sacrificing solution quality. Experiments using Kinect as well as Velodyne data show that, our proposed method is faster than existing methods, while obtaining solutions comparable to standard ICP. An additional benefit is robustness to parameters when processing data from different sensors.
Another avenue of research are methods that improve the ability of ICP to handle an unknown degree of overlap between the point clouds. This includes the use of a trimmed square cost function to estimate optimal transformation @cite_21 , rejection of corresponding point pairs based on a threshold defined by the standard deviation of point pair distances @cite_15 , or the use of a semi differential invariant for correspondence estimation @cite_9 .
@cite_9 @cite_15 @cite_21
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A method for matching 3-D curves under Euclidean motions is presented. Our approach uses a semi-differential invariant description requiring only first derivatives and one reference point, thus avoiding the computation of high order derivatives. A novel curve similarity measure building on the notion of spl epsiv -reciprocal correspondence is proposed. It is shown that by combining spl epsiv -reciprocal correspondence with the robust least median of squares motion estimation, the registration of partially occluded curves can be accomplished. An experiment with real curves extracted from 3-D surfaces demonstrates that curve matching can be successfully performed even on data from a simple and cheap 3-D sensor. > Robust registration of two 3-D point sets is a common problem in computer vision. The iterative closest point (ICP) algorithm is undoubtedly the most popular algorithm for solving this kind of problem. In this paper, we present the Picky ICP algorithm, which has been created by merging several extensions of the standard ICP algorithm, thus improving its robustness and computation time. Using pure 3-D point sets as input data, we do not consider additional information like point color or neighborhood relations. In addition to the standard ICP algorithm and the Picky ICP algorithm proposed in this paper, a robust algorithm due to Masuda and Yokoya and the RICP algorithm by are evaluated. We have experimentally determined the basin of convergence, robustness to noise and outliers, and computation time of these four ICP based algorithms. The problem of geometric alignment of two roughly preregistered, partially overlapping, rigid, noisy 3D point sets is considered. A new natural and simple, robustified extension of the popular Iterative Closest Point (ICP) algorithm (Besl and McKay, 1992) is presented, called the Trimmed ICP (TrICP). The new algorithm is based on the consistent use of the least trimmed squares (LTS) approach in all phases of the operation. Convergence is proved and an efficient implementation is discussed. TrICP is fast, applicable to overlaps under 50 , robust to erroneous measurements and shape defects, and has easy-to-set parameters. ICP is a special case of TrICP when the overlap parameter is 100 . Results of testing the new algorithm are shown.