Efficient Octree-Based Volumetric SLAM Supporting Signed-Distance and Occupancy Mapping

Emanuele Vespa, Nikolay Nikolov, Marius Grimm, Luigi Nardi, Paul H.J. Kelly, Stefan Leutenegger

Research output: Contribution to journalArticlepeer-review

59 Scopus citations


We present a dense volumetric simultaneous localisation and mapping (SLAM) framework that uses an octree representation for efficient fusion and rendering of either a truncated signed distance field (TSDF) or an occupancy map. The primary aim of this letter is to use one single representation of the environment that can be used not only for robot pose tracking and high-resolution mapping, but seamlessly for planning. We show that our highly efficient octree representation of space fits SLAM and planning purposes in a real-time control loop. In a comprehensive evaluation, we demonstrate dense SLAM accuracy and runtime performance on-par with flat hashing approaches when using TSDF-based maps, and considerable speed-ups when using occupancy mapping compared to standard occupancy maps frameworks. Our SLAM system can run at 10-40 Hz on a modern quadcore CPU, without the need for massive parallelization on a GPU. We, furthermore, demonstrate a probabilistic occupancy mapping as an alternative to TSDF mapping in dense SLAM and show its direct applicability to online motion planning, using the example of informed rapidly-exploring random trees (RRT∗).

Original languageEnglish
Article number8255617
Pages (from-to)1144-1151
Number of pages8
JournalIEEE Robotics and Automation Letters
Issue number2
StatePublished - Apr 2018
Externally publishedYes


  • Mapping
  • simultaneous localisation and mapping (SLAM)
  • visual-based navigation


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