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Sensors 2011, 11(11), 10197-10219;

A Novel Combined SLAM Based on RBPF-SLAM and EIF-SLAM for Mobile System Sensing in a Large Scale Environment

School of Information Science and Engineering, Ocean University of China, 238 Songling Road, Qingdao 266100, China
School of Mechanical & Electrical Engineering, China Jiliang University, 258 Xueyuan Street, Xiasha High-Edu Park, Hangzhou 310018, China
Authors to whom correspondence should be addressed.
Received: 11 August 2011 / Revised: 27 September 2011 / Accepted: 27 October 2011 / Published: 28 October 2011
(This article belongs to the Section Sensor Networks)
PDF [1144 KB, uploaded 21 June 2014]


Mobile autonomous systems are very important for marine scientific investigation and military applications. Many algorithms have been studied to deal with the computational efficiency problem required for large scale Simultaneous Localization and Mapping (SLAM) and its related accuracy and consistency. Among these methods, submap-based SLAM is a more effective one. By combining the strength of two popular mapping algorithms, the Rao-Blackwellised particle filter (RBPF) and extended information filter (EIF), this paper presents a Combined SLAM—an efficient submap-based solution to the SLAM problem in a large scale environment. RBPF-SLAM is used to produce local maps, which are periodically fused into an EIF-SLAM algorithm. RBPF-SLAM can avoid linearization of the robot model during operating and provide a robust data association, while EIF-SLAM can improve the whole computational speed, and avoid the tendency of RBPF-SLAM to be over-confident. In order to further improve the computational speed in a real time environment, a binary-tree-based decision-making strategy is introduced. Simulation experiments show that the proposed Combined SLAM algorithm significantly outperforms currently existing algorithms in terms of accuracy and consistency, as well as the computing efficiency. Finally, the Combined SLAM algorithm is experimentally validated in a real environment by using the Victoria Park dataset. View Full-Text
Keywords: RBPF-SLAM; EIF-SLAM; submap; consistency; computational efficiency RBPF-SLAM; EIF-SLAM; submap; consistency; computational efficiency
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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He, B.; Zhang, S.; Yan, T.; Zhang, T.; Liang, Y.; Zhang, H. A Novel Combined SLAM Based on RBPF-SLAM and EIF-SLAM for Mobile System Sensing in a Large Scale Environment. Sensors 2011, 11, 10197-10219.

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