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

Paper


Low, Tobias and Manzanera, Antoine. 2010. "Ground-plane classification for robot navigation: combining multiple cues toward a visual-based learning system." 11th International Conference on Control, Automation, Robotics and Vision (ICARCV 2010). Singapore 07 - 10 Dec 2010 United States. https://doi.org/10.1109/ICARCV.2010.5707289
Paper/Presentation Title

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

Presentation TypePaper
AuthorsLow, Tobias (Author) and Manzanera, Antoine (Author)
Journal or Proceedings TitleProceedings of the 11th International Conference on Control, Automation, Robotics and Vision (ICARCV 2010)
Number of Pages6
Year2010
Place of PublicationUnited States
ISBN9781424478149
9781424478156
Digital Object Identifier (DOI)https://doi.org/10.1109/ICARCV.2010.5707289
Web Address (URL) of Paperhttps://ieeexplore.ieee.org/document/5707289
Web Address (URL) of Conference Proceedingshttps://ieeexplore.ieee.org/xpl/conhome/5702939/proceeding
Conference/Event11th International Conference on Control, Automation, Robotics and Vision (ICARCV 2010)
Event Details
Rank
A
A
A
Event Details
11th International Conference on Control, Automation, Robotics and Vision (ICARCV 2010)
Event Date
07 to end of 10 Dec 2010
Event Location
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.

Keywordsimage classification; image disparity; ground plane; obstacle avoidance; visual navigation; mobile robots
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 2020400703. Autonomous vehicle systems
400799. Control engineering, mechatronics and robotics not elsewhere classified
469999. Other information and computing sciences not elsewhere classified
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Byline AffiliationsDepartment of Mechanical and Mechatronic Engineering
Superior National School of Advanced Techniques, France
Institution of OriginUniversity of Southern Queensland
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