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.
@article{arxiv.2311.00626,
title = {nvblox: GPU-Accelerated Incremental Signed Distance Field Mapping},
author = {Alexander Millane and Helen Oleynikova and Emilie Wirbel and Remo Steiner and Vikram Ramasamy and David Tingdahl and Roland Siegwart},
journal= {arXiv preprint arXiv:2311.00626},
year = {2024}
}