  2D LiDAR SLAM (Simultaneous Localization and Mapping) is widely used in
indoor environments due to its stability and flexibility. However, its mapping
procedure is usually operated by a joystick in static environments, while
indoor environments often are dynamic with moving objects such as people. The
generated map with noisy points due to the dynamic objects is usually
incomplete and distorted. To address this problem, we propose a framework of
2D-LiDAR-based SLAM without manual control that effectively excludes dynamic
objects (people) and simplify the process for a robot to map an environment.
The framework, which includes three parts: people tracking, filtering and
following. We verify our proposed framework in experiments with two classic
2D-LiDAR-based SLAM algorithms in indoor environments. The results show that
this framework is effective in handling dynamic objects and reducing the
mapping error.
