nvblox: GPU-Accelerated Incremental Signed Distance Field Mapping
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 w...
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Main Authors | , , , , , , |
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Format | Journal Article |
Language | English |
Published |
01.11.2023
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Subjects | |
Online Access | Get full text |
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Summary: | 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. |
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DOI: | 10.48550/arxiv.2311.00626 |