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

Low, Tobias 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.


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: c 2010 IEEE. Permanent restricted access to published version due to publisher copyright policy.
Faculty/School / Institute/Centre: Historic - Faculty of Engineering and Surveying - Department of Mechanical and Mechatronic Engineering
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 : 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
Socio-Economic Objective: A Defence > 81 Defence > 8101 Defence > 810105 Intelligence
Identification Number or DOI: 10.1109/ICARCV.2010.5707289

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