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

机译:基于视觉显着性的移动机器人障碍物区域3D感知方法

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摘要

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.
机译:提出了一种基于改进显着区域提取(ISRE)的室内环境下移动机器人3D感知障碍区域方法。该模型通过Kinect传感器获取原始图像,然后通过显着性过滤算法从原始图像中获取原始显着图(OSM)和强度特征图(IFM)。 IFM被用作PCNN的输入中子。为了使点火范围更加精确,对PCNN点火脉冲输入进行了如下改进:在PCNN内部神经元和OSM二值化显着图像之间采用点乘法算法。然后我们确定了最终的点火脉冲输入。显着性二值化区域抽象最终通过改进的PCNN多次迭代得以实现。最后,将二值化区域映射到Kinect传感器获得的深度图上,移动机器人可以实现障碍物定位功能。该方法在移动机器人(Pioneer3-DX)上进行。实验结果证明了该算法的可行性和有效性。

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  • 来源
    《Journal of robotics》 |2015年第2015期|720174.1-720174.10|共10页
  • 作者单位

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

    College of Electronic Information & Control Engineering, Beijing University of Technology, Beijing 100124, China,Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing 100124, China,Engineering Research Center of Digital Community, Ministry of Education, Beijing 100124, China;

    College of Electronic Information & Control Engineering, Beijing University of Technology, Beijing 100124, China,Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing 100124, China,Engineering Research Center of Digital Community, Ministry of Education, Beijing 100124, China;

    College of Electronic Information & Control Engineering, Beijing University of Technology, Beijing 100124, China,Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing 100124, China,Engineering Research Center of Digital Community, Ministry of Education, Beijing 100124, China;

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