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.
翻译:低成本、小形式因素和轻量固态LiDAR传感器的出现为自主无人驾驶飞行器(UAVs)带来了新的机会,提高了导航安全和计算效率。尽管基于LiDAR的UAV的成功发展必须依靠广泛的模拟。现有的模拟器由于难以获得的密集网状地图的要求,很难对真实世界环境进行模拟。在本文中,我们为基于LIDAR的模拟机模拟机(UAVS)模拟了真实世界场景。关键理念是潜在的点定位方法,我们直接从点云图中绘制深度图像,并进行内部插插插图,以获得现实的LIDAR点测量。我们开发的模拟器能够运行一个轻量的计算平台,支持以不同的分辨率和扫描模式、动态障碍和多功能系统模拟LIDAR的模拟。在ROS平台上开发的模拟器可以很容易地与其他自主机器人的关键模块进行沟通,例如感知、状态估计、规划和控制。最后,我们开发的Slodal-AVAVA(LA)的深度图像的深度的深度图像可以提供10个高分辨率计算结果。在现实的图像、历史环境上展示的图像、历史环境的图像,以及各种历史环境可以提供高分辨率的图像,为10个高分辨率的图像的图像、历史环境提供高分辨率和高分辨率的模拟器。