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

Publikation: Beitrag in FachzeitschriftArtikelBegutachtung

59 Zitate (Scopus)

Abstract

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∗).

OriginalspracheEnglisch
Aufsatznummer8255617
Seiten (von - bis)1144-1151
Seitenumfang8
FachzeitschriftIEEE Robotics and Automation Letters
Jahrgang3
Ausgabenummer2
DOIs
PublikationsstatusVeröffentlicht - Apr. 2018
Extern publiziertJa

Fingerprint

Untersuchen Sie die Forschungsthemen von „Efficient Octree-Based Volumetric SLAM Supporting Signed-Distance and Occupancy Mapping“. Zusammen bilden sie einen einzigartigen Fingerprint.

Dieses zitieren