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Indexed by:期刊论文

Date of Publication:2015-01-01

Journal:IEEE SENSORS JOURNAL

Included Journals:SCIE、EI、Scopus

Volume:15

Issue:1

Page Number:37-47

ISSN No.:1530-437X

Key Words:Active environment perception; robust 3-D object recognition; scale coordination; semantic information; 3-D laser scanning; service robot

Abstract: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.