  The LiDAR and inertial sensors based localization and mapping are of great
significance for Unmanned Ground Vehicle related applications. In this work, we
have developed an improved LiDAR-inertial localization and mapping system for
unmanned ground vehicles, which is appropriate for versatile search and rescue
applications. Compared with existing LiDAR-based localization and mapping
systems such as LOAM, we have two major contributions: the first is the
improvement of the robustness of particle swarm filter-based LiDAR SLAM, while
the second is the loop closure methods developed for global optimization to
improve the localization accuracy of the whole system. We demonstrate by
experiments that the accuracy and robustness of the LiDAR SLAM system are both
improved. Finally, we have done systematic experimental tests at the Hong Kong
science park as well as other indoor or outdoor real complicated testing
circumstances, which demonstrates the effectiveness and efficiency of our
approach. It is demonstrated that our system has high accuracy, robustness, as
well as efficiency. Our system is of great importance to the localization and
mapping of the unmanned ground vehicle in an unknown environment.
