IEEE SENSORS JOURNAL, VOL. 15, NO. 1, JANUARY 2015 37 Using Scale Coordination and Semantic Information for Robust 3-D Object Recognition by a Service Robot Yan Zhuang, Member, IEEE, Xueqiu Lin, Huosheng Hu, Senior Member, IEEE, and Ge Guo Member, IEEE 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. Index Terms— Active environment perception, robust 3-D object recognition, scale coordination, semantic information, 3-D laser scanning, service robot. I. I NTRODUCTION A CTIVE environment perception and 3-D object recog- nition are two fundamental tasks for service robots to operate in cluttered indoor environments [1]–[3], including detecting tables, chairs and sofas in normal operations [4], [5], and detecting hazard and dangerous objects in search and rescue operations [6], [7]. A variety of computer vision algo- rithms and novel RGB-D vision sensors have been developed to implement visual object recognition, resulting in significant progress in 3-D object recognition [8]–[10]. Lai et al. define a view-to-object distance where a novel view is compared simul- taneously to all views of a previous object [11]. This novel distance is based on a weighted combination of feature dif- ferences between views, which leads to superior classification performance on object category and instance recognition in Manuscript received May 26, 2014; accepted July 4, 2014. Date of publi- cation July 15, 2014; date of current version November 5, 2014. This work was supported by the National Natural Science Foundation of China under Grant 61375088 and Grant 61035005. The associate editor coordinating the review of this paper and approving it for publication was Prof. Subhas Chandra Mukhopadhyay. Y. Zhuang and X. Lin are with the School of Control Science and Engineering, Dalian University of Technology, Dalian 116024, China (e-mail: zhuang@dlut.edu.cn; 373930883@qq.com). H. Hu is with the School of Computer Science and Electronic Engineering, University of Essex, Colchester CO4 3SQ, U.K. (e-mail: hhu@essex.ac.uk). G. Guo is with the School of Information Science and Technology, Dalian Maritime University, Dalian 116026, China (e-mail: geguo@dlut.edu.cn). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/JSEN.2014.2336987 the context of RGB-D cameras. Herbst et al. use a PrimeSense camera to provide color and depth, and fuse information from multiple sensing modalities to detect changes between two 3-D maps [12]. To effectively perform active environment perception, col- ored 3-D point cloud data obtained from a range sensor and an associated color camera has been successfully used in object detection and recognition. In [5], objects were detected by using Iterative Closest Point (ICP) with a database of known point cloud models to guarantee accurate results. By using a 3-D point cloud and an associated color image, a fast scene analysis scheme was presented in [13], which can rapidly parse a scene into a collection of planar surfaces so that a robot was able to quickly detect relevant objects such as walls, doors, windows, tables and chairs. To automatically search objects in an indoor environment, Kanezaki et al. developed a system that can collect 3-D-scene data by transforming both color and range images into a set of color voxel data. 3-D features in each bounding box region were extracted for computing the similarity between these features and the features of a target object. Then a global search of the collected 3-D-scene data objects was conducted for quick object detection [14]. Although the fusion between the 3-D point cloud and an associated color image (e.g. an RGB-D (Kinect-style) depth cameras) is a very useful technology, these sensors still have two limitations for object detection and recognition. First, the RGB data acquired from a RGB-D camera are still susceptible to different lighting conditions; Second, their measurement range is limited and the field of view is narrow. Therefore, many 3-D laser range finders have been deployed for handling large-scale scenes, such as Leica HDS 3000 terrestrial laser scanner, Velodyne’s HDL-64E LiDAR sensor, as well as 2D SICK and Hokuyo laser sensors mounted on rotating platforms. These 3-D laser range finders can provide a wider field of view, detailed 3-D range information, most importantly working in a dark environment. In recent years, laser range point clouds have been widely deployed in object detection and recognition. A common approach for finding objects in 3-D laser range point clouds is to use a bottom-up procedure where planes and curves are located first, and then fit to known models. Rather than asso- ciating point data with a priori hypothesized object, an alter- native approach is to reconstruct object models directly from point data, and explores relaxations of the exact likelihood function [15]. In order to extract effective features in 3-D point cloud data for object recognition, Steder et al. proposed 1530-437X © 2014 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.