Temporally scalable visual SLAM using a reduced pose graph
Author(s)Johannsson, Hordur; Kaess, Michael; Fallon, Maurice Francis; Leonard, John Joseph
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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.
DepartmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory; Massachusetts Institute of Technology. Department of Mechanical Engineering
Proceedings of the 2013 IEEE International Conference on Robotics and Automation
Institute of Electrical and Electronics Engineers (IEEE)
Johannsson, Hordur, Michael Kaess, Maurice Fallon, and John J. Leonard. “Temporally Scalable Visual SLAM Using a Reduced Pose Graph.” 2013 IEEE International Conference on Robotics and Automation (May 2013).