Title :
Using planar features for fast localization in indoor environments
Author :
Li Ma ; Cheung, Ernest C. H. ; Newman, Wyatt
Author_Institution :
Univ. of Hong Kong, Hong Kong, China
Abstract :
A means for fast and accurate indoor localization using point-cloud data is presented. A sparse environment model is proposed, comprised of a list of planar patches. It is shown that, with this simplified model, incoming point-cloud data can be associated rapidly with one (or none) of the constituent planes, and the environment model can be fit to the clustered sample points algebraically in a least-squares sense in real time.
Keywords :
computer graphics; control engineering computing; indoor navigation; least squares approximations; mobile robots; pattern clustering; clustered sample points; constituent planes; fast localization; indoor environments; least-squares sense; planar features; planar patches; point-cloud data; sparse environment model; Computational modeling; Mathematical model; Robot sensing systems; Solid modeling; Three-dimensional displays; Transforms;
Conference_Titel :
Robotics and Biomimetics (ROBIO), 2014 IEEE International Conference on
DOI :
10.1109/ROBIO.2014.7090530
