Triangle mesh maps have proven to be an efficient 3D environment representation, allowing robots to navigate, indoors as well as in challenging outdoor environments with tunnels, hills and varying slopes. However, any robot navigating autonomously necessarily requires reliable, accurate, and continuous localization in such a mesh map where it plans its paths and missions. We present Mesh ICP Localization (MICP-L), a novel and computationally lightweight method for registering one or more range sensors to a triangle mesh map to continuously localize a robot in 6D even in GPS-denied environments. Simulative Projective Correspondences (SPC) between a range sensor and mesh map are found through simulations accelerated with latest NVIDIA RTX hardware. The optimization of initially guessed poses is performed in parallel even with combined data coming from different range sensors attached to the robot. With this work, we aim to significantly advance the developments in mesh-based localization for autonomous robotic applications. MICP-L is open source and fully integrated with ROS and tf.
翻译:三角形网格图已被证明是一种有效的三维环境表示方法,可以使机器人在室内和具有隧道、山丘和不同坡度的具有挑战性的室外环境中导航。然而,任何自主导航的机器人都需要在该网格图中进行可靠、准确和连续的定位,其中它规划其路径和任务。我们提出了网格ICP定位(MICP-L)一种新颖的、计算轻量化的方法,用于将一个或多个距离传感器注册到三角网格地图上,以在6D中连续定位机器人,即使在缺乏GPS信号的环境中也可以。通过使用最新的NVIDIA RTX硬件加速的仿真,找到了距离传感器和网格地图之间的仿射对应。在并行处理中,对最初猜测的姿态进行优化,即使来自机器人上不同的距离传感器的组合数据也可以做到。通过这项研究,我们旨在显著推进基于网格的自主机器人应用。MICP-L是开源的,并且与ROS和tf完全集成。