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http://dx.doi.org/10.5302/J.ICROS.2014.14.9024

Past and State-of-the-Art SLAM Technologies  

Song, Jae-Bok (Mechanical Engineering, Korea University)
Hwang, Seo-Yeon (Mechanical Engineering, Korea University)
Publication Information
Journal of Institute of Control, Robotics and Systems / v.20, no.3, 2014 , pp. 372-379 More about this Journal
Abstract
This paper surveys past and state-of-the-art SLAM technologies. The standard methods for solving the SLAM problem are the Kalman filter, particle filter, graph, and bundle adjustment-based methods. Kalman filters such as EKF (Extended Kalman Filter) and UKF (Unscented Kalman Filter) have provided successful results for estimating the state of nonlinear systems and integrating various sensor information. However, traditional EKF-based methods suffer from the increase of computation burden as the number of features increases. To cope with this problem, particle filter-based SLAM approaches such as FastSLAM have been widely used. While particle filter-based methods can deal with a large number of features, the computation time still increases as the map grows. Graph-based SLAM methods have recently received considerable attention, and they can provide successful real-time SLAM results in large urban environments.
Keywords
SLAM; Kalman filter; particle filter; GraphSLAM; bundle adjustment; mobile robot;
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