nvblox: GPU-Accelerated Incremental Signed Distance Field Mapping
OPEN ACCESS
Author / Producer
Date
2024
Publication Type
Conference Paper
ETH Bibliography
yes
Citations
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OPEN ACCESS
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Rights / License
Abstract
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 177× speed-up in surface reconstruction, and up to a 31× improvement in distance field computation, and is available open-source.
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Publication status
published
Editor
Book title
2024 IEEE International Conference on Robotics and Automation (ICRA)
Journal / series
Volume
Pages / Article No.
2698 - 2705
Publisher
IEEE
Event
41st IEEE International Conference on Robotics and Automation (ICRA 2024)
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
robotics
Organisational unit
03737 - Siegwart, Roland Y. / Siegwart, Roland Y.
02284 - NFS Digitale Fabrikation / NCCR Digital Fabrication