Ground-plane classification for robot navigation: combining multiple cues toward a visual-based learning system

Low, Tobias ORCID: https://orcid.org/0000-0001-8666-0517 and Manzanera, Antoine (2010) Ground-plane classification for robot navigation: combining multiple cues toward a visual-based learning system. In: 11th International Conference on Control, Automation, Robotics and Vision (ICARCV 2010), 7-10 Dec 2010, Singapore.


Abstract

This paper describes a vision-based ground-plane classification system for autonomous indoor mobile-robot that takes advantage of the synergy in combining together multiple visual-cues. A priori knowledge of the environment is important in many biological systems, in parallel with their reactive systems. As such, a learning model approach is taken here for the classification of the ground/object space, initialised through a new Distributed-Fusion (D-Fusion) method that captures colour and textural data using Superpixels. A Markov Random Field (MRF) network is then used to classify, regularise, employ a priori constraints, and merge additional ground/object information provided by other visual cues (such as motion) to improve classification images. The developed system can classify indoor test-set ground-plane surfaces with an average true-positive to false-positive rate of 90.92% to 7.78% respectively on test-set data. The system has been designed in mind to fuse a variety of different visual-cues. Consequently it can be customised to fit different situations and/or sensory architectures accordingly.


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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: © 2010 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Faculty/School / Institute/Centre: Historic - Faculty of Engineering and Surveying - Department of Mechanical and Mechatronic Engineering (Up to 30 Jun 2013)
Faculty/School / Institute/Centre: Historic - Faculty of Engineering and Surveying - Department of Mechanical and Mechatronic Engineering (Up to 30 Jun 2013)
Date Deposited: 16 Mar 2011 05:25
Last Modified: 20 Feb 2015 04:50
Uncontrolled Keywords: image classification; image disparity; ground plane; obstacle avoidance; visual navigation; mobile robots
Fields of Research (2008): 09 Engineering > 0913 Mechanical Engineering > 091303 Autonomous Vehicles
09 Engineering > 0906 Electrical and Electronic Engineering > 090602 Control Systems, Robotics and Automation
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080199 Artificial Intelligence and Image Processing not elsewhere classified
Fields of Research (2020): 40 ENGINEERING > 4007 Control engineering, mechatronics and robotics > 400703 Autonomous vehicle systems
40 ENGINEERING > 4007 Control engineering, mechatronics and robotics > 400799 Control engineering, mechatronics and robotics not elsewhere classified
46 INFORMATION AND COMPUTING SCIENCES > 4699 Other information and computing sciences > 469999 Other information and computing sciences not elsewhere classified
Socio-Economic Objectives (2008): A Defence > 81 Defence > 8101 Defence > 810105 Intelligence
Identification Number or DOI: https://doi.org/10.1109/ICARCV.2010.5707289
URI: http://eprints.usq.edu.au/id/eprint/18681

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