Obstacles Regions 3D-Perception Method for Mobile Robots Based on Visual Saliency. (8th December 2015)
- Record Type:
- Journal Article
- Title:
- Obstacles Regions 3D-Perception Method for Mobile Robots Based on Visual Saliency. (8th December 2015)
- Main Title:
- Obstacles Regions 3D-Perception Method for Mobile Robots Based on Visual Saliency
- Authors:
- Xu, Tao
Jia, Songmin
Dong, Zhengyin
Li, Xiuzhi - Other Names:
- Watanabe Keigo Academic Editor.
- Abstract:
- Abstract : 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.
- Is Part Of:
- Journal of robotics. Volume 2015(2015)
- Journal:
- Journal of robotics
- Issue:
- Volume 2015(2015)
- Issue Display:
- Volume 2015, Issue 2015 (2015)
- Year:
- 2015
- Volume:
- 2015
- Issue:
- 2015
- Issue Sort Value:
- 2015-2015-2015-0000
- Page Start:
- Page End:
- Publication Date:
- 2015-12-08
- Subjects:
- Robotics -- Periodicals
Robotics
Periodicals
629.892 - Journal URLs:
- https://www.hindawi.com/journals/jr/ ↗
- DOI:
- 10.1155/2015/720174 ↗
- Languages:
- English
- ISSNs:
- 1687-9600
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library HMNTS - ELD Digital store
- Ingest File:
- 10726.xml