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.
Selecting corresponding points in the source and reference point clouds is an important step in ICP, as evidenced by the various ICP variants that have been proposed in the literature to improve this part of ICP. @cite_7 use simulated annealing to obtain a good initial starting point. Masuda and Yokoyo @cite_16 combine a least median square estimator and ICP using random samples of points in each iteration. While this method uses random sub-sampling, the goal is to remove outliers from the point cloud processed by ICP and does not perform incremental updates of the transformation estimate as our proposed method does.
@cite_16 @cite_7
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Registration and segmentation of multiple range images are important problems in range image analysis. We propose a new algorithm of range data registration and segmentation that is robust in the presence of outlying points (outliers) like noise and occlusion. The registration algorithm determines rigid motion parameters from a pair of range images. Our method is an integration of the iterative closest point (ICP) algorithm with random sampling and least median of squares (LMS or LMedS) estimator. The segmentation method classifies the input data points into four categories comprising inliers and 3 types of outliers. Finally, we integrate the inliers obtained from multiple range images to construct a data set representing an entire object. We have experimented with our method both on synthetic range images and on real range images taken by two kinds of rangefinders. The proposed method does not need preliminary processes such as smoothing or trimming of isolated points because of its robustness. It also offers the advantage of reducing the computational cost. The need to register data is abundant in applications such as: world modeling, part inspection and manufacturing, object recognition, pose estimation, robotic navigation, and reverse engineering. Registration occurs by aligning the regions that are common to multiple images. The largest difficulty in performing this registration is dealing with outliers and local minima while remaining efficient. A commonly used technique, iterative closest point, is efficient but is unable to deal with outliers or avoid local minima. Another commonly used optimization algorithm, simulated annealing, is effective at dealing with local minima but is very slow. Therefore, the algorithm developed in this paper is a hybrid algorithm that combines the speed of iterative closest point with the robustness of simulated annealing. Additionally, a robust error function is incorporated to deal with outliers. This algorithm is incorporated into a complete modeling system that inputs two sets of range data, registers the sets, and outputs a composite model.