Real-time large-scale dense RGB-D SLAM with volumetric fusion
Name
Leonard_Real-time.pdf
Size
30.77 MB
Format
Adobe PDF
Checksum (MD5)
4a07f7b3679bcfc4ee9cd73544bccb38
Author(s)
Whelan, Thomas
Kaess, Michael
Johannsson, Hordur
Fallon, Maurice Francis
Leonard, John Joseph
McDonald, John
Date Issued
December 2014
Journal
The International Journal of Robotics Research
Publisher
Sage Publications
Citation
Whelan, T., M. Kaess, H. Johannsson, M. Fallon, J. J. Leonard, and J. McDonald. “Real-Time Large-Scale Dense RGB-D SLAM with Volumetric Fusion.” The International Journal of Robotics Research 34, no. 4–5 (April 1, 2015): 598–626.
Version
Author's final manuscript
Abstract
We present a new simultaneous localization and mapping (SLAM) system capable of producing high-quality globally consistent surface reconstructions over hundreds of meters in real time with only a low-cost commodity RGB-D sensor. By using a fused volumetric surface reconstruction we achieve a much higher quality map over what would be achieved using raw RGB-D point clouds. In this paper we highlight three key techniques associated with applying a volumetric fusion-based mapping system to the SLAM problem in real time. First, the use of a GPU-based 3D cyclical buffer trick to efficiently extend dense every-frame volumetric fusion of depth maps to function over an unbounded spatial region. Second, overcoming camera pose estimation limitations in a wide variety of environments by combining both dense geometric and photometric camera pose constraints. Third, efficiently updating the dense map according to place recognition and subsequent loop closure constraints by the use of an ‘as-rigid-as-possible’ space deformation. We present results on a wide variety of aspects of the system and show through evaluation on de facto standard RGB-D benchmarks that our system performs strongly in terms of trajectory estimation, map quality and computational performance in comparison to other state-of-the-art systems.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Mechanical Engineering
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1177/0278364914551008
