Efficient octree-based volumetric SLAM supporting signed-distance and occupancy mapping
File(s)
Accepted version
Author(s)
Type
Journal Article
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*).
Date Issued
2018-04-01
Date Acceptance
2017-12-26
Citation
IEEE Robotics and Automation Letters, 2018, 3 (2), pp.1144-1151
ISSN
2377-3766
Publisher
Institute of Electrical and Electronics Engineers
Start Page
1144
End Page
1151
Journal / Book Title
IEEE Robotics and Automation Letters
Volume
3
Issue
2
Copyright Statement
© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Sponsor
Engineering & Physical Science Research Council (E
Engineering & Physical Science Research Council (EPSRC)
Engineering & Physical Science Research Council (EPSRC)
Grant Number
PO: ERZ1820653
EP/N018494/1
EP/P010040/1
Subjects
Science & Technology
Technology
Robotics
Mapping
simultaneous localisation and mapping (SLAM)
visual-based navigation
Publication Status
Published
Date Publish Online
2018-01-12