  The emergence of low-cost, small form factor and light-weight solid-state
LiDAR sensors have brought new opportunities for autonomous unmanned aerial
vehicles (UAVs) by advancing navigation safety and computation efficiency. Yet
the successful developments of LiDAR-based UAVs must rely on extensive
simulations. Existing simulators can hardly perform simulations of real-world
environments due to the requirements of dense mesh maps that are difficult to
obtain. In this paper, we develop a point-realistic simulator of real-world
scenes for LiDAR-based UAVs. The key idea is the underlying point rendering
method, where we construct a depth image directly from the point cloud map and
interpolate it to obtain realistic LiDAR point measurements. Our developed
simulator is able to run on a light-weight computing platform and supports the
simulation of LiDARs with different resolution and scanning patterns, dynamic
obstacles, and multi-UAV systems. Developed in the ROS framework, the simulator
can easily communicate with other key modules of an autonomous robot, such as
perception, state estimation, planning, and control. Finally, the simulator
provides 10 high-resolution point cloud maps of various real-world
environments, including forests of different densities, historic building,
office, parking garage, and various complex indoor environments. These
realistic maps provide diverse testing scenarios for an autonomous UAV.
Evaluation results show that the developed simulator achieves superior
performance in terms of time and memory consumption against Gazebo and that the
simulated UAV flights highly match the actual one in real-world environments.
We believe such a point-realistic and light-weight simulator is crucial to
bridge the gap between UAV simulation and experiments and will significantly
facilitate the research of LiDAR-based autonomous UAVs in the future.
