Automatic Place Determination using Colour Histograms and Self-Organising Maps
Werner, Felix, Sitte, Joaquin, & Maire, Frederic D. (2007) Automatic Place Determination using Colour Histograms and Self-Organising Maps. In 13th International Conference on Advanced Robotics, 21-24 August 2007, Jeju, Korea Republic.
In this paper we propose a model-free appearance-based method to automatically determine places as landmarks for topological navigation. Most of the current approaches for automatically selecting landmarks are based on template models or complex feature detectors. We use modified colour histograms, more precisely an entropy-constrained 3D~colour clustering as appearance-based image features which adapt to the colour distribution of the environment. An unsupervised neural network learning strategy is used to automatically determine places by clustering the modified histograms. Results from experiments in an indoor environment with a robot equipped with a panoramic camera show that the places, which were clustered in histogram space refer to physically close positions in the world domain in a large degree and can be used as landmarks for navigation purposes.
Impact and interest:
Citation countsare sourced monthly fromand citation databases.
These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.
Citations counts from theindexing service can be viewed at the linked Google Scholar™ search.
Full-text downloadsdisplays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.
Repository Staff Only: item control page