DocumentCode :
49311
Title :
Spatial Density Patterns for Efficient Change Detection in 3D Environment for Autonomous Surveillance Robots
Author :
Vieira, Antonio W. ; Drews, Paulo L. J. ; Campos, Mario F. M.
Author_Institution :
Comput. Sci. Dept., Univ. Fed. de Minas Gerais, Belo Horizonte, Brazil
Volume :
11
Issue :
3
fYear :
2014
fDate :
Jul-14
Firstpage :
766
Lastpage :
774
Abstract :
The ability to detect changes is an essential competence that robots should possess for increased autonomy. In several applications, such as surveillance, a robot needs to detect relevant changes in the environment by comparing current sensory data with previously acquired information from the environment. We present an efficient method for point cloud comparison and change detection in 3D environments based on spatial density patterns. Our method automatically segments 3D data corrupted by noise and outliers into an implicit volume bounded by a surface, making it possible to efficiently apply Boolean operations in order to detect changes and to update existing maps. The method has been validated on several trials using mobile robots operating in real environments and its performance was compared to state-of-the-art algorithms. Our results demonstrate the performance of the proposed method, both in greater accuracy and reduced computational cost.
Keywords :
image segmentation; mobile robots; object detection; robot vision; 3D data segmentation; 3D environment; Boolean operations; autonomous surveillance robots; change detection; implicit volume; mobile robots; point cloud comparison; spatial density patterns; Accuracy; Data models; Density functional theory; Noise; Robot sensing systems; Three-dimensional displays; Change detection; point cloud; surveillance;
fLanguage :
English
Journal_Title :
Automation Science and Engineering, IEEE Transactions on
Publisher :
ieee
ISSN :
1545-5955
Type :
jour
DOI :
10.1109/TASE.2013.2294851
Filename :
6702510
Link To Document :
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