Speeding Up Iterative Closest Point Using Stochastic Gradient Descent
This work addresses the need for efficient point cloud alignment in robotics applications such as autonomous driving, though it is incremental as it builds on the widely used ICP method.
The paper tackles the problem of aligning 3D point clouds for tasks like registration and SLAM by proposing a method that uses stochastic gradient descent to speed up iterative closest point optimization, achieving faster convergence without sacrificing solution quality in experiments with Kinect and Velodyne data.
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.