OctoMap-RT: Fast Probabilistic Volumetric Mapping Using Ray-Tracing GPUs

Heajung Min, Kyung Min Han, Young J. Kim

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


A 3D occupancy map that is accurately modeled after real-world environments is essential for reliably performing robotic tasks. Probabilistic volumetric mapping (PVM) is a well-known environment mapping method using volumetric voxel grids that represent the probability of occupancy. The main bottleneck of current CPU-based PVM, such as OctoMap, is determining voxel grids with occupied and free states using ray-shooting. In this letter, we propose an octree-based PVM, called OctoMap-RT, using a hybrid of off-the-shelf ray-tracing GPUs and CPUs to substantially improve CPU-based PVM. OctoMap-RT employs massively parallel ray-shooting using GPUs to generate occupied and free voxel grids and to update their occupancy states in parallel, and it exploits CPUs to restructure the PVM using the updated voxels. Our experiments using various large-scale real-world benchmarking environments with dense and high-resolution sensor measurements demonstrate that OctoMap-RT builds maps up to 41.2 times faster than OctoMap and 9.3 times faster than the recent SuperRay CPU implementation. Moreover, OctoMap-RT constructs a map with 0.52% higher accuracy, in terms of the number of occupancy grids, than both OctoMap and SuperRay.

Original languageEnglish
Pages (from-to)5696-5703
Number of pages8
JournalIEEE Robotics and Automation Letters
Issue number9
StatePublished - 1 Sep 2023

Bibliographical note

Publisher Copyright:
© 2016 IEEE.


  • hardware -software integration in robotics
  • Mapping
  • simulation and animation


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