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Journal of Robotics
Volume 2015, Article ID 720174, 10 pages
Research Article

Obstacles Regions 3D-Perception Method for Mobile Robots Based on Visual Saliency

Tao Xu,1,2,3,4 Songmin Jia,1,3,4 Zhengyin Dong,1,3,4 and Xiuzhi Li1,3,4

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

Received 19 August 2015; Revised 19 November 2015; Accepted 19 November 2015

Academic Editor: Keigo Watanabe

Copyright © 2015 Tao Xu 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.


A novel mobile robots 3D-perception obstacle regions method in indoor environment based on Improved Salient Region Extraction (ISRE) is proposed. This model acquires the original image by the Kinect sensor and then gains Original Salience Map (OSM) and Intensity Feature Map (IFM) from the original image by the salience filtering algorithm. The IFM was used as the input neutron of PCNN. In order to make the ignition range more exact, PCNN ignition pulse input was further improved as follows: point multiplication algorithm was taken between PCNN internal neuron and binarization salience image of OSM; then we determined the final ignition pulse input. The salience binarization region abstraction was fulfilled by improved PCNN multiple iterations finally. Finally, the binarization area was mapped to the depth map obtained by Kinect sensor, and mobile robot can achieve the obstacle localization function. The method was conducted on a mobile robot (Pioneer3-DX). The experimental results demonstrated the feasibility and effectiveness of the proposed algorithm.