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Journal of Sensors
Volume 2016, Article ID 3243842, 18 pages
Research Article

A Novel Improved Probability-Guided RANSAC Algorithm for Robot 3D Map Building

Songmin Jia,1,2,3 Ke Wang,1,2,3 Xiuzhi Li,1,2,3 and Tao Xu1,2,3,4

1College of Electronic and Control Engineering, Beijing University of Technology, Beijing 100124, China
2Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing 100124, China
3Engineering Research Center of Digital Community, Ministry of Education, Beijing 100124, China
4School of Mechanical and Electrical Engineering, Henan Institute of Science and Technology, Xinxiang 453003, China

Received 4 August 2015; Revised 19 February 2016; Accepted 16 March 2016

Academic Editor: Maan E. El Najjar

Copyright © 2016 Songmin Jia et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This paper presents a novel improved RANSAC algorithm based on probability and DS evidence theory to deal with the robust pose estimation in robot 3D map building. In this proposed RANSAC algorithm, a parameter model is estimated by using a random sampling test set. Based on this estimated model, all points are tested to evaluate the fitness of current parameter model and their probabilities are updated by using a total probability formula during the iterations. The maximum size of inlier set containing the test point is taken into account to get a more reliable evaluation for test points by using DS evidence theory. Furthermore, the theories of forgetting are utilized to filter out the unstable inliers and improve the stability of the proposed algorithm. In order to boost a high performance, an inverse mapping sampling strategy is adopted based on the updated probabilities of points. Both the simulations and real experimental results demonstrate the feasibility and effectiveness of the proposed algorithm.