DocumentCode :
3514129
Title :
Temporally scalable visual SLAM using a reduced pose graph
Author :
Johannsson, H. ; Kaess, Michael ; Fallon, M. ; Leonard, John J.
Author_Institution :
Comput. Sci. & Artificial Intell. Lab., Massachusetts Inst. of Technol., Cambridge, MA, USA
fYear :
2013
fDate :
6-10 May 2013
Firstpage :
54
Lastpage :
61
Abstract :
In this paper, we demonstrate a system for temporally scalable visual SLAM using a reduced pose graph representation. Unlike previous visual SLAM approaches that maintain static keyframes, our approach uses new measurements to continually improve the map, yet achieves efficiency by avoiding adding redundant frames and not using marginalization to reduce the graph. To evaluate our approach, we present results using an online binocular visual SLAM system that uses place recognition for both robustness and multi-session operation. Additionally, to enable large-scale indoor mapping, our system automatically detects elevator rides based on accelerometer data. We demonstrate long-term mapping in a large multi-floor building, using approximately nine hours of data collected over the course of six months. Our results illustrate the capability of our visual SLAM system to map a large are over extended period of time.
Keywords :
SLAM (robots); graph theory; pose estimation; accelerometer data; indoor mapping; online binocular visual SLAM system; pose graph representation; reduced pose graph; temporally scalable visual SLAM; Cameras; Optimization; Robustness; Simultaneous localization and mapping; Visualization;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Robotics and Automation (ICRA), 2013 IEEE International Conference on
Conference_Location :
Karlsruhe
ISSN :
1050-4729
Print_ISBN :
978-1-4673-5641-1
Type :
conf
DOI :
10.1109/ICRA.2013.6630556
Filename :
6630556
Link To Document :
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