Research Article Indoor SLAM Using Laser and Camera with Closed-Loop Controller for NAO Humanoid Robot Shuhuan Wen, 1 Kamal Mohammed Othman, 2 Ahmad B. Rad, 2 Yixuan Zhang, 2 and Yongsheng Zhao 3 1 Key Lab of Industrial Computer Control Engineering of Hebei Province, Yanshan University, Qinhuangdao 066004, China 2 School of Engineering Sciences, Simon Fraser University, No. 250-13450, 102 Avenue, Surrey, BC, Canada V3T 0A3 3 Parallel Robot and Mechatronic System Laboratory of Hebei Province and Key Laboratory of Advanced Forging & Stamping Technology and Science of Ministry of National Education, Yanshan University, Qinhuangdao 066004, China Correspondence should be addressed to Ahmad B. Rad; arad@sfu.ca Received 4 May 2014; Revised 24 June 2014; Accepted 25 June 2014; Published 10 July 2014 Academic Editor: Shen Yin Copyright © 2014 Shuhuan Wen et al. Tis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. We present a SLAM with closed-loop controller method for navigation of NAO humanoid robot from Aldebaran. Te method is based on the integration of laser and vision system. Te camera is used to recognize the landmarks whereas the laser provides the information for simultaneous localization and mapping (SLAM ). K-means clustering method is implemented to extract data from diferent objects. In addition, the robot avoids the obstacles by the avoidance function. Te closed-loop controller reduces the error between the real position and estimated position. Finally, simulation and experiments show that the proposed method is efcient and reliable for navigation in indoor environments. 1. Introduction Robot has been used in many areas, such as industry process [1–6] and autonomous navigation. Autonomous navigation in an unknown environment is regarded as a key attribute of a service robot and has received considerable attention in the last two decades. Te focus of this paper is the indoor envi- ronments such as homes, ofces, and hospitals. It is important that the service robots can roam around in order to assist humans or to perform various tasks in such surroundings. For example, the robots need to detect and avoid obstacles that they might encounter in the environment. Precise position estimation is a necessary prerequisite for a reliable navigation. In this case, simultaneous local- ization and mapping (SLAM) approach is employed to make a robot truly autonomous without the need for any a priori knowledge of location. SLAM algorithm [7, 8] can be recognized and localized in an unknown environment. Some artifcial intelligence methods are used in the SLAM of the mobile robots, such as reinforcement learning [9], which we will study for NAO humanoid robot in the future. SLAM algorithm is the essential process of building a map of the environment while simultaneously determining its location within this map. SLAM has also been implemented in a number of diferent domains from indoor to outdoor, underwater, and airborne systems. However, the efectiveness of various SLAM methods greatly depends on using diferent exteroceptive sensors (sonar, laser, camera, odometer, etc.). Some methods are based on vision or laser [10, 11]. Camera and laser have diferent features, respectively. Cameras can provide the 3D sensing of the environment and obstacle avoidance, but its accuracy of localization is generally inferior to laser scanner. Compared to other sensors, laser provides more accurate range and bearing measurements. However, laser cannot provide the image of the environment. In particular, laser of the robot cannot recognize the object when it is faced with landmarks or obstacles. Terefore, the combination of camera and laser can achieve very good and reliable navigation for indoor SLAM problems. Tere are some studies reported in the literature whereby the laser and the stereo camera are used together for accomplishing indoor navigation tasks. In Labayrade et al.’s paper [12], the Hindawi Publishing Corporation Abstract and Applied Analysis Volume 2014, Article ID 513175, 8 pages http://dx.doi.org/10.1155/2014/513175