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Using scale coordination and semantic information for robust 3-D object recognition by a service robot

Zhuang, Y and Lin, X and Hu, H and Guo, G (2015) 'Using scale coordination and semantic information for robust 3-D object recognition by a service robot.' IEEE Sensors Journal, 15 (1). 37 - 47. ISSN 1530-437X

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Abstract

© 2014 IEEE. This paper presents a novel 3-D object recognition framework for a service robot to eliminate false detections in cluttered office environments where objects are in a great diversity of shapes and difficult to be represented by exact models. Laser point clouds are first converted to bearing angle images and a Gentleboost-based approach is then deployed for multiclass object detection. In order to solve the problem of variable object scales in object detection, a scale coordination technique is adopted in every subscene that is segmented from the whole scene according to the spatial distribution of 3-D laser points. Moreover, semantic information (e.g., ceilings, floors, and walls) extracted from raw 3-D laser points is utilized to eliminate false object detection results. K-means clustering and Mahalanobis distance are finally deployed to perform object segmentation in a 3-D laser point cloud accurately. Experiments were conducted on a real mobile robot to show the validity and performance of the proposed method.

Item Type: Article
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Jim Jamieson
Date Deposited: 08 Jul 2015 14:22
Last Modified: 23 Jan 2019 00:18
URI: http://repository.essex.ac.uk/id/eprint/14100

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