论文标题
MICP-L：基于网格的ICP，用于使用硬件加速射线铸造机器人本地化
MICP-L: Mesh-based ICP for Robot Localization using Hardware-Accelerated Ray Casting
论文作者
论文摘要
三角形网格地图是一种多功能的3D环境表示形式，用于机器人在挑战的室内和室外环境中导航，展示了隧道，丘陵和不同的斜坡。为了利用这些网格地图，需要使用方法来准确地将机器人定位在此类地图中，以执行路径计划和导航等重要任务。我们提出了网格ICP定位（MICP-L），这是一种新颖且具有计算高效的方法，用于将一个或多个范围传感器注册到三角形网格地图上，即使在GPS贬低的环境中，也可以连续将机器人定位在6D中。我们通过支持不同的平行计算设备（例如多核CPU，GPU和最新的NVIDIA RTX硬件）来加速范围传感器和网格图之间射线铸造对应关系（RCC）的计算。通过将协方差计算转换为还原操作，我们可以在CPU或GPU上并行优化初始猜测姿势，从而实现我们的实现，可实时适用于许多体系结构。我们证明了我们的本地化方法与农业，空中和汽车领域的数据集的鲁棒性。
Triangle mesh maps are a versatile 3D environment representation for robots to navigate in challenging indoor and outdoor environments exhibiting tunnels, hills and varying slopes. To make use of these mesh maps, methods are needed to accurately localize robots in such maps to perform essential tasks like path planning and navigation. We present Mesh ICP Localization (MICP-L), a novel and computationally efficient 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. We accelerate the computation of ray casting correspondences (RCC) between range sensors and mesh maps by supporting different parallel computing devices like multicore CPUs, GPUs and the latest NVIDIA RTX hardware. By additionally transforming the covariance computation into a reduction operation, we can optimize the initial guessed poses in parallel on CPUs or GPUs, making our implementation applicable in real-time on many architectures. We demonstrate the robustness of our localization approach with datasets from agricultural, aerial, and automotive domains.
