Dense, volumetric maps are essential to enable robot navigation and interaction with the environment. To achieve low latency, dense maps are typically computed onboard the robot, often on computationally constrained hardware. Previous works leave a gap between CPU-based systems for robotic mapping which, due to computation constraints, limit map resolution or scale, and GPU-based reconstruction systems which omit features that are critical to robotic path planning, such as computation of the Euclidean Signed Distance Field (ESDF). We introduce a library, nvblox, that aims to fill this gap, by GPU-accelerating robotic volumetric mapping. Nvblox delivers a significant performance improvement over the state of the art, achieving up to a 177x speed-up in surface reconstruction, and up to a 31x improvement in distance field computation, and is available open-source.

翻译：摘要：稠密体积地图对于机器人导航与环境交互至关重要。为实现低延迟，稠密地图通常在机器人机载计算资源受限的硬件上计算。现有研究在以下两方面存在空白：基于CPU的机器人建图系统受限于计算能力，限制了地图分辨率或规模；而基于GPU的重建系统则省略了对机器人路径规划至关重要的功能，如欧几里得有符号距离场（ESDF）的计算。我们提出nvblox库，通过GPU加速机器人体积建图，旨在填补这一空白。Nvblox在性能上显著超越现有技术水平，表面重建速度提升高达177倍，距离场计算速度提升高达31倍，并以开源形式发布。