The ability to determine its positions and orientations accurately is very important for a mobile robot when performing tasks such as free-range path tracking and navigation. In this paper, a cost-effective method is proposed for localization of a mobile robot, where a low-resolution charge-coupled device (CCD) camera, a stepping motor, and three artificial landmarks are used. The CCD camera is mounted on the robot and its rotation is controlled by the stepping motor. Then, from the images captured by the CCD camera, image-processing techniques are used to determine the distances between the CCD camera and the three landmarks. On the basis of these distances, it can be proved that the position of the robot is determined uniquely provided that the three artificial landmarks observed are not installed in a line. Moreover, the heading angle of the robot is determined from the rotation angle of the stepping motor. To account for possible installation errors, a non-linear programming method is proposed. Meanwhile, a least-squares method is also used to increase the accuracy of localization. To show the feasibility of the proposed method, experimental results are included for illustration.
BorensteinJ.EverettH. R.FengL.Where am I? (sensors and methods for mobile robot positioning), 1996 (University of Michigan Press, Ann Arbor, Michigan).
2.
FigueroaF.MahajanA.A robust navigation system for autonomous vehicles using ultrasonicsControl Engng Practice, 1994, 2, 49–59.
3.
ZhaoF. J.GuoH. J.AbeK. I.A mobile robot localization using ultrasonic sensors in indoor environmentIn Proceedings of the Sixth IEEE International Workshop on Robot and human communication, 1997, pp. 52–57 (IEEE, New York).
4.
TsaiC. C.A localization system of a mobile robot by fusing dead-reckoning and ultrasonic measurementsIEEE Trans. Instrumentation Measmt, 1998, 47, 1399–1404.
5.
ZhaoF. J.GuoH. J.AbeK. I.Mobile robot localization using two sonar sensors and one natural landmark. In Proceedings of the 37th SICE Annual Conference, 1998, pp. 893–898.
6.
CanouJ.NovalesC.PoissonG.MarcheP.Quick primitives extraction using inertia matrix on measures issue from an ultrasonic networkIn Proceedings of the 2001 IEEE International Conference on Robotics and automation, 2001, pp. 3999–4004 (IEEE, New York).
7.
WuC. J.TsaiC. C.Localization of an autonomous mobile robot based on ultrasonic sensory informationJ. Intell. Robotic System, 2001, 30, 267–277.
8.
LinH. H.TsaiC. C.HsuJ. C.ChangC. F.Ultrasonic self-localization and pose tracking of an autonomous mobile robot via fuzzy adaptive extended information filteringIn Proceedings of the 2003 IEEE International Conference on Robotics and automation, 2003, pp. 1283–1290 (IEEE, New York).
9.
ForsbergJ.LarssonU.WernerssonA.Mobile robot navigation using the range-weighted Hough transformIEEE Robotics Automn Mag., 1995, 2, 18–26.
10.
FoxD.BurgardW.ThrunS.Markov localization for mobile robots in dynamic environmentsJ. Artif. Intell. Res., 1999, 11, 391–427.
11.
JensfeltP.ChristensenH. I.Pose tracking using laser scanning and minimalistic environmental modelsIEEE Trans. Robotics Automn, 2001, 17, 138–147.
12.
JensfeltP.KristensenS.Active global localization for a mobile robot using multiple hypothesis trackingIEEE Trans. Robotics Automn, 2001, 17, 748–760.
13.
ThrunS.FoxD.BurgardW.DellaertF.Robust Monte Carlo localization for mobile robotsArtif. Intell., 128, 99–141.
14.
DeckerS.GanderH.VinczeM.PrenningerJ. P.Dynamic measurement of position and orientation of robotsIEEE Trans. Instrumentation Measmt, 1992, 41, 897–901.
15.
NishizawaT.OhyaA.YutaS.An implementation of on-board position estimation for a mobile robotIn Proceedings of the IEEE International Conference on Robotics and automation, 1995, pp. 395–400 (IEEE, New York).
16.
ChangC. F.TsaiC. C.HsuJ. C.LinC. C.Laser pose tracking for a mobile robot using fuzzy adaptive extended information filtering. In Proceedings of the American Control Conference, 2003, pp. 2471–2476 (IEEE New York).
17.
BetkeM.GurvitsL.Mobile robot localization using landmarksIn Proceedings of the IEEE International Conference on Intelligent robots and systems, 1994, pp. 135–142 (IEEE, New York).
18.
CauchoisC.BrassartE.MarhicB.DrocourtC.An absolute localization method using a synthetic panoramic image baseIn Proceedings of Third Workshop on Omnidirectional vision, 2002, pp. 128–135.
19.
DaoN. X.YouJ. B.OhS. R.HwangboM.Visual self-localization for indoor mobile robots using natural linesIn Proceedings of the IEEE International Conference on Intelligent robots and systems, 2003, pp. 1252–1257 (IEEE, New York).
20.
GasparJ.WintersN.Santos-VictorJ.Vision-based navigation and environmental representations with an omni-directional cameraIEEE Trans. Robotics Autom, 2000, 16, 890–898.
21.
KimB. H.RohD. K.LeeJ. M.LeeM. H.SonK.LeeM. C.ChoiJ. W.HanS. H.Localization of a mobile robot using images of a moving targetIn Proceedings of the IEEE International Conference on Robotics and automation, 2001, pp. 253–258 (IEEE, New York).
22.
MarquesC. F.LimaP. U.A localization method for a soccer robot using a vision-based omni-directional sensorRoboCup 2001 - Robot Soccer World Cup IV, 2001, pp. 96–107.
23.
SeS.LoweD.LittleJ.Vision-based mobile robot localization and mapping using scale-invariant featuresIn Proceedings of the IEEE International Conference on Robotics and automation, 2001, pp. 2051–2058 (IEEE, New York).
24.
WolfJ.BurgardW.BurkhardtH.Robust vision-based localization for mobile robots using an image retrieval system based on invariant featuresIn Proceedings of the IEEE International Conference on Robotics and automation, 2002, pp. 359–365 (IEEE, New York).
25.
ZhouC.WeiY.TanT.Mobile robot self-localization based on global visual appearance featuresIn Proceedings of the IEEE International Conference on Robotics and automation, 2003, pp. 1271–1276 (IEEE, New York).
26.
KolmanB.HillD. R.Elementary linear algebra, 7th edition, 1999 (Prentice-Hall, Englewood Cliffs, New Jersey).
27.
CraigJ. J.Introduction to robotics: mechanics and control, 2nd edition, 1989 (Addison-Wesley, Reading, Massachusetts).
28.
29.
TruccoE.VerriA.Introductory techniques for 3-D computer vision, 1999 (Prentice-Hall, Englewood Cliffs, New Jersey).
30.
ShapiroL. G.StockmanG. C.Computer vision, 2001 (Prentice-Hall, Englewood Cliffs, New Jersey).
