  Localization, or position fixing, is an important problem in robotics
research. In this paper, we propose a novel approach for long-term localization
in a changing environment using 3D LiDAR. We first create the map of a real
environment using GPS and LiDAR. Then, we divide the map into several small
parts as the targets for cloud registration, which can not only improve the
robustness but also reduce the registration time. PointLocalization allows us
to fuse different kinds of odometers, which can optimize the accuracy and
frequency of localization results. We evaluate our algorithm on an unmanned
ground vehicle (UGV) using LiDAR and a wheel encoder, and obtain the
localization results at more than 20 Hz after fusion. The algorithm can also
localize the UGV in a 180-degree field of view (FOV). Using an outdated map
captured six months ago, this algorithm shows great robustness, and the test
results show that it can achieve an accuracy of 10 cm. PointLocalization has
been tested for a period of more than six months in a crowded factory and has
operated successfully over a distance of more than 2000 km.
