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：三角形网格地图已被证明是一种高效的三维环境表示方式，可使机器人在室内以及包含隧道、丘陵和变坡环境的复杂户外场景中实现导航。然而，自主导航的机器人必须在此类规划路径和任务的网格地图中实现可靠、精确且连续的定位。我们提出了一种名为MICP-L的新型轻量级计算方法，该方法通过将一个或多个测距传感器与三角形网格地图进行配准，即使在GPS受限环境中也能实现机器人的六自由度连续定位。通过利用最新的NVIDIA RTX硬件加速仿真，我们建立了测距传感器与网格地图之间的仿真投影对应关系（SPC）。即使同时处理来自机器人上多个测距传感器的融合数据，也能并行实现初始猜测位姿的优化。本研究旨在显著推动基于网格地图的自主机器人定位技术发展。MICP-L已开源，并完全集成于ROS和tf框架。