Localization algorithm using a virtual label for a mobile robot in indoor and outdoor environments

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1 Artif Life Robotics (2011) 16: ISAROB 2011 DOI /s ORIGINAL ARTICLE Ki Ho Yu Min Cheol Lee Jung Hun Heo Youn Geun Moon Localization algorithm using a virtual label for a mobile robot in indoor and outdoor environments Received: March 29, 2011 / Accepted: March 31, 2011 Abstract Scanning laser range sensors provide range data consisting of a set of point measurements. The laser sensor URG-04LX has a distance range of approximately m and a scanning angle range of 240. Usually, such an image range is acquired from one viewpoint by moving the laser beam using rotating mirrors/prisms. The orientation of the laser beam can easily be measured and converted into the coordinates of the image. This article conducts localization using virtual labels with data about distances in the environment obtained from 2D distance laser sensors. This method puts virtual labels on special features and points which are along the mobile robot s path. The current location is calculated by combining the virtual label and the range image of the laser range finder. Key words Virtual label Localization Laser range finder Encoder 1 Introduction Ubiquitous location-based services offer helpful services at any time and anywhere by using real-time location information about objects based on a ubiquitous network, and these are growing in importance. Autonomous mobile robots can be a solution for various applications related to service robots. In early mobile robot technologies, localization for autonomous mobile robots was widely researched and many methods were developed, such as a dead reckoning system, an active badge system, an active beacon system, and localization systems using a landmark, radio frequency identification (RFID), or a global positioning system (GPS). 1 8 K.H. Yu M.C. Lee (*) J.H. Heo Y.G. Moon Department of Mechanical Engineering, Pusan National University, Busan, Geumjeong Gu , Republic of Korea mclee@pusan.ac.kr This work was presented in part and was awarded Best Paper Award at the 16th International Symposium on Artificial Life and Robotics, Oita, Japan, January 27 29, 2011 The dead-reckoning system is one well-known localization method based on measuring relative positions. The dead-reckoning system has the advantage of a high sampling rate and outstanding accuracy over short intervals. However, this system cannot avoid an inevitable accumulation of errors because of the time-integral term between current direction and distance. In particular, the cumulative error in the direction angle may cause an infinitely large positioning error as time passes. 1,2 In order to detect the location of people inside a building, an active badge system with infrared rays has been researched. However, this system has problems such as the short communication distance and a weak performance against sunshine. 4 An active beacon system with ultrasonic sensors is known for its relatively accurate performance and low power consumption. However, this method can be influenced by any obstacle between the transmitter and the receiver, the external temperature, and noise. 5 The localization method using a landmark or RFID is robust to changes in the external environment such as temperature or solar light. However, it has a fatal defect in that it can be applied only to a limited space where a RFID tag or landmark can be attached. In addition, it is impossible to use it outdoors. 3,4 To recognize an absolute position outdoors, a GPS is the most powerful localization method. However, it is limited to the area of the GPS signal and has a low positional accuracy. 6 A meaningful and still unsolved problem for most applications is to develop a robust positioning system. Therefore, this article is concerned with how to localize and navigate a mobile robot without any landmarks, or in a limited detection range, in an unknown environment. To solve these problems, we propose a new localization algorithm using a virtual label algorithm. The proposed method is based on virtual labeling techniques. Here, we use a laser range finder and an encoder in an indoor environment. In an outdoor environment, a GPS alone is adopted to calculate the initial position. This article is divided into the following sections: generation of range finder scans (Sect. 2), localization with a virtual label algorithm (Sect. 3), experimental results of implemen-

2 362 Fig. 2. Distance data of the laser range finder action Fig. 1. Laser range finder and indoor environment Table 1. Specifications of URG-04LX Specification Measuring area Accuracy (repeatability) Angular resolution Scanning time tation within a typical indoor/outdoor scene (Sect. 4), and our conclusions and future work (Sect. 5). 2 Laser range-finder scanning mm, mm ± 10 mm mm, 70 mm Step angle 0.36 (360 /1024 steps) 100 ms/scan We constructed a mobile robot equipped with a laser range finder, as shown in Fig. 1. A 2D laser range finder (URG- 04LX) made by Hokuyo Co. was used. The URG-04LX has a 100-ms cycle time, and 765 resolutions of distance from 60 mm to 4095 mm divided by The specifications of this laser range finder are given in Table 1. Figure 2 shows the output data of the laser range finder in an indoor environment. This sensor is able to scan 240 Fig. 3. Distance data conversion of 2D coordinates and 765 items of distance output data in each cycle of 100 ms. Here, the localization is calculated in areas covering 210. A total of 600 items of distance output data are used to obtain 2D coordinates, including a virtual label, as shown in Fig. 3. These distance output data are transformed into 2D coordinates using Eqs. 1 and 2. xi = Di i (cos( )), 05. π i = 0, 1, 2,..., 600 yi = Di i (sin( )), 05. π i = 0, 1, 2,..., 600 where θ i ( ) is the angular resolution from 1 point to 600 points, and D i is the distance output data. Figure 3 shows the distance conversion of 2D coordinates. It is necessary to classify a movable area (Mk x,y ) and a non-movable area (Nk x,y ) in transformed coordinates (x i,y i ). In Fig. 4, Nk xy is used to map the virtual label, and Mk xy is (1) (2)

3 363 Fig. 4. Segmentation area used to determine the path of the mobile robot. After determining Mk x,y and Nk x,y, a map is generated by considering the radius of the robot (R r ), the distance that the robot has moved (M d ), and the direction of the robot (R θ ). By repeating and accumulating the process proposed above using Eqs. 3 and 4, a virtual label is immediately added to the map. X Y map map = Nk j x (3) = Nk j y (4) By using the virtual label algorithm proposed, which is explained in Sect. 3, a map is effectively generated in either an indoor or an outdoor environment. For outdoor circumstances only, GPS is also used to get an initial position. 3 Virtual label algorithm Before moving the mobile robot, a virtual label should be allocated on the map, which can be generated by scanning the current circumstances with the laser range finder. In the circumstances, a virtual label does not need to be on real points, lines, or spaces. As the name virtual implies, the label can be generated on virtual structures which do not exist in real space. Then a temporary virtual wall is allocated in the left-hand and right-hand 45 of space. The proposed virtual label method is carried out with the following procedure. Step 1. In order to initialize the current position of the mobile robot, scan the current circumstances. Step 2. Place the label on the map using the scanned data, feature points, lines, and spaces. Step 3. If feature points, lines, or spaces cannot be found, a virtual wall and a virtual label will be generated on the map, as shown in Fig. 5. The virtual walls are built on both Fig. 5. Virtual labeling for indoor environment sides,. and the virtual labels (VLm x, VLm y ) are attached on the virtual walls at a position on a 45 degree diagonal position on the virtual walls. Step 4. Let the current position of the robot be a position localized by using the distance (D n ) and the angle (R θ ) from the label. Step 5. A movable domain (M1 x,y, M2 x,y ) and an immovable domain (N1 x,y, N2 x,y ) have to be added on the map when the label is in place. Step 6. After moving the mobile robot, returned to Step 1. The position of the mobile robot is acquired by using the distance D n and VLm x,y. The current position is calculated by using Eqs. 5 and (6). R = ( VLm ± D ) (5) R x x n y = ( VLm ) (6) y where D n is the distance from the mobile robot to the virtual label (VLm x,y ). Figure 6 shows the flow chart for the virtual labeling procedure. 4 Experiments In order to evaluate the localization and map formation of the proposed virtual label algorithm, some experiments were carried out with a moving mobile robot in an indoor environment (Fig. 1) and an outdoor environment (Fig. 7). 4.1 Indoor experiment In an indoor environment, the virtual labeling algorithm was applied to generate the map for localization. The map obtained with the virtual label algorithm is shown in Fig. 8.

4 364 Fig. 8. Map building in an indoor environment Fig. 6. Block diagram of virtual labeling Fig. 7. Outdoor environment The distance between the starting point and the goal was a straight 10-m path. The test conditions were selected in order to compare the localization effect using encoder data with the localization effect using the virtual label algorithm in an indoor environment. The speed of the mobile robot was 0.54 m/s. The test result with the virtual label algorithm is shown in Fig. 9. After the experiment had been carried out five times, the location error of the mobile robot was found to be about ±2 cm. In a previous study using an encoder, the repetitive Fig. 9. Moving trajectory of a mobile robot with the virtual labeling algorithm indoor error was about ±3 cm. However, localization using encoder data is not much different. Therefore, the experiment shows that the virtual label algorithm is a precise localization method. 4.2 Outdoor experiment The virtual labeling algorithm was also applied to generate maps for localization in an outdoor environment with a

5 365 Fig. 10. Map building in an outdoor environment mobile robot equipped with a laser range finder and a GPS. A GPS sensor alone was used to calculate the initial position. The map obtained by applying the virtual label algorithm is shown in Fig. 10. The distance between the starting point and the goal was a straight 17-m path. The speed of the mobile robot was 0.54 m/s. The result of covering the course five times is shown in Fig. 11. If there is no space to make a label in the scanning data of the range finder, a label is allocated to a virtual wall for localization. After the experiment was carried out five times, the repetitive error of the mobile robot was about ±4 cm because the outdoor road was rough. The repetitive error using the encoder only was about ±10 cm. Therefore, localization with the virtual label algorithm is superior to the localization method with the GPS. 5 Conclusions In order to localize and build a map in both indoor and outdoor environments, we proposed a localization algorithm with a virtual label algorithm. We carried out an experimental test using a mobile robot in order to evaluate the proposed virtual label algorithm. In the experimental test, the proposed virtual label algorithm gave a precise localization result where the location error of the mobile robot was about ±2 cm in an indoor environment and about ±4 cm in an outdoor environment. In the future, we hope to improve this to give a maximum localization error within ±5 mm in both indoor and outdoor environments. In addi- Fig. 11. Moving trajectory of a mobile robot with the virtual labeling algorithm outdoors tion, we will compare this method with other localization algorithms. Acknowledgments This research was supported by the MKE (Ministry of Knowledge Economy), Korea, under the human resources development program for a robotics support program supervised by the NIPA (National IT Industry Promotion Agency) (NIPA (C )). This research was supported by the MKE (The Ministry of Knowledge Economy), Korea, under the Specialized Field Navigation / Localization Technology Research Center support program supervised by the NIPA (National IT Industry Promotion Agency). (NIPA-2010-(C )). References 1. Dellaert F, Fox D, Burgard W, et al (1999) Monte Carlo localization for mobile robots. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) 2. Buschka P, Saffiotti A (2002) A virtual sensor for room detection. Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems EPFL 3. Lee HJ, Lee MC (2006) Localization of mobile robot based on radio frequency identification devices SICE-ICASE International Joint Conference 4. Borenstein J, Feng L (1996) Measurement and corrections of systematic odometry errors in mobile robots. IEEE Trans Robotics Autom 12:6 5. Venet T, Capitaine T, Hamzaoui M, et al (2002) One active beacon for an indoor absolute localization of a mobile vehicle. IEEE International Conference on Robotics and Automation, Washington, May 6. Borenstein J, Everett HR, Feng L, et al (1997) Mobile robot positioning: sensors and techniques. J Robot Syst 14(4): Diosi A, Taylor G, Kleeman L (2005) Interactive SLAM using laser and advanced sonar. Proceedings of the IEEE International Conference on Robotics and Automation 8. Misono Y, Goto Y, Tarutoko Y, et al (2007) Development of laser rangefinder-based SLAM algorithm for mobile robot navigation. SICE Annual Conference

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