Paper Info
Title
Real-time 6-DOF multi-session visual SLAM over large-scale environments
Abstract
This paper describes a system for performing real-time multi-session visual mapping in large-scale environments. Multi-session mapping considers the problem of combining the results of multiple simultaneous localisation and mapping (SLAM) missions performed repeatedly over time in the same environment. The goal is to robustly combine multiple maps in a common metrical coordinate system, with consistent estimates of uncertainty. Our work employs incremental smoothing and mapping (iSAM) as the underlying SLAM state estimator and uses an improved appearance-based method for detecting loop closures within single mapping sessions and across multiple sessions. To stitch together pose graph maps from multiple visual mapping sessions, we employ spatial separator variables, called anchor nodes, to link together multiple relative pose graphs. The system architecture consists of a separate front-end for computing visual odometry and windowed bundle adjustment on individual sessions, in conjunction with a back-end for performing the place recognition and multi-session mapping. We provide experimental results for real-time multi-session visual mapping on wheeled and handheld datasets in the MIT Stata Center. These results demonstrate key capabilities that will serve as a foundation for future work in large-scale persistent visual mapping.
Year
DOI
Venue
2013
10.1016/j.robot.2012.08.008
Robotics and Autonomous Systems
Keywords
Field
DocType
Bundle adjustment,Place recognition,Stereo,Anchor nodes,iSAM
Coordinate system,Computer vision,Graph,Visual odometry,State estimator,Computer science,Simulation,Bundle adjustment,Mobile device,Smoothing,Artificial intelligence,Systems architecture
Journal
Volume
Issue
ISSN
61
10
0921-8890
Citations 
PageRank 
References 
19
0.92
24
Authors
5
Name
Order
Citations
PageRank
John McDonald11339.70
Michael Kaess2180799.52
Cesar Dario Cadena Lerma343730.12
J. Neira450939.39
John J. Leonard54696431.59