  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.
