NDLI: Simultaneous localization and mapping survey based on filtering techniques

Content Provider	IEEE Xplore Digital Library
Author	Tang Swee Ho Yeong Che Fai Ming, E.S.L.
Copyright Year	2015
Description	Author affiliation: Centre of Artificial Intell. & Robot. (CAIRO), Univ. Teknol. Malaysia, Skudai, Malaysia (Yeong Che Fai) \|\| Fac. of Electr. Eng., Univ. Teknol. Malaysia, Skudai, Malaysia (Ming, E.S.L.) \|\| Malaysia-Japan Int. Inst. of Technol., Univ. Teknol. Malaysia, Kuala Lumpur, Malaysia (Tang Swee Ho)
Abstract	Simultaneous Localization and Mapping (SLAM) problem has been an active area of research in robotics for more than two decades. This paper reviews SLAM based on different filtering techniques used to do the state estimation of the mobile robot. The filtering techniques included in this study are Kalman filter, particle filter, H infinity filter. It can be concluded that each filtering technique has its own advantages and disadvantages as it is very dependent on the situations. Kalman filter is much suitable for dealing with Gaussian distribution. Particle filter is selected for large-scale environment as its computation complexity is logarithmic compared to Kalman filter which has quadratic complexity. H infinity filter is used to improve the convergence of SLAM system.
Starting Page	1
Ending Page	6
File Size	596499
Page Count	6
File Format	PDF
ISBN	9781479978625
DOI	10.1109/ASCC.2015.7244836
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-05-31
Publisher Place	Malaysia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Mobile Robot Simultaneous localization and mapping SLAM Kalman Filter Particle Filter Noise Particle filters H infinity filter Simultaneous Localization and Mapping Kalman filters Mobile robots
Content Type	Text
Resource Type	Article

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