title: 3-D Laser-Based Multiclass and Multiview Object Detection in Cluttered Indoor Scenes.
abstract: This paper investigates the problem of multiclass and multiview 3-D object detection for service robots operating in a cluttered indoor environment. A novel 3-D object detection system using laser point clouds is proposed to deal with cluttered indoor scenes with a fewer and imbalanced training data. Raw 3-D point clouds are first transformed to 2-D bearing angle images to reduce the computational cost, and then jointly trained multiple object detectors are deployed to perform the multiclass and multiview 3-D object detection. The reclassification technique is utilized on each detected low confidence bounding box in the system to reduce false alarms in the detection. The RUS-SMOTEboost algorithm is used to train a group of independent binary classifiers with imbalanced training data. Dense histograms of oriented gradients and local binary pattern features are combined as a feature set for the reclassification task. Based on the dalian university of technology (DUT)-3-D data set taken from various office and household environments, experimental results show the validity and good performance of the proposed method.
authors.forename: Xuesong
authors.initials: X
authors.lastname: Zhang
authors.forename: Yan
authors.initials: Y
authors.lastname: Zhuang
authors.forename: Huosheng
authors.initials: H
authors.lastname: Hu
authors.forename: Wei
authors.initials: W
authors.lastname: Wang