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1. USING SCALE COORDINATION AND SEMANTIC INFORMATION FOR ROBUST 3-
D OBJECT RECOGNITION BY A SERVICE ROBOT
ABSTRACT
This paper presents a novel 3-D object recognitionframework for a service robot to
eliminate false detectionsin cluttered office environments where objects are in a greatdiversity of
shapes and difficult to be represented by exact models.Laser point clouds are first converted to
bearing angle imagesand a Gentleboost-based approach is then deployed for multiclassobject
detection. In order to solve the problem of variableobject scales in object detection, a scale
coordination techniqueis adopted in every sub scene that is segmented from the wholescene
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 falseobject
detection results. K-means clustering and Mahalanobis distance are finally deployed to perform
object segmentation ina 3-D laser point cloud accurately. Experiments were conductedon a real
mobile robot to show the validity and performance ofthe proposed method.