A key problem of mobile robots is that of the positioning capability in order to detect their locations in the operating environment. A promising technology used in indoor localization recently is Visible Light Communication (VLC). In this paper, an integrated Angle of Arrival-Received Signal Strength (AOA-RSS) localization method using the VLC, which could get high accuracy with simple hardware, is proposed. In addition, the Extended Kalman Filter (EKF) and Particle Filter (PF) algorithms are combined with the proposed AOA-RSS localization method in order to achieve higher accuracy of the mobile robot positioning. By computer simulation, the performance of the proposed AOA-RSS localization method combined with the EKF and PF algorithms are compared. The combination scheme outperforms the individual VLC localization with small error approximation of a few centimetres.
AchourKDjekouneA (2012) Localization and guidance with an embarked camera on a mobile robot. Advanced Robotics Journal16(1): 87–102.
2.
AngelMRosaF (2009) Experimental comparison of Extended Kalman and Particle Filter in mobile robotic localization. In: the CERMA’09 conference on electronics robotics and automotive mechanics, Cuernavaca, pp.157–162.
3.
ArmingolJMEscaleraAMorenoL. (2012) Mobile robot localization using a non-linear evolutionary filter. Advanced Robotics Journal16(7): 629–652.
4.
CenGMatsuhiraNHirokawaJ. (2009) New entropy-based adaptive particle filter for mobile robot localization. Advanced Robotics Journal23(12–13): 1761–1778.
5.
CheungKWSoHCMaWK. (2004) Least squares algorithms for time-of-arrival-based mobile location. IEEE Transactions on Signal Processing52(4): 1121–1130.
6.
ChoiMChoiJChungWK (2012a) Correlation-based scan matching using ultrasonic sensors for EKF localization. Advanced Robotics Journal26(13): 1495–1519.
7.
ChoiYHParkIHKimYH. (2012b) Novel LBS Technique based on visible light communications. In: the 2012 IEEE international conference on consumer electronics, Las Vegas, pp.576–577.
8.
DuongPMVanNTTHoangTT. (2013) Localization of networked robot systems subject to random delay and packet loss. In: the IEEE/ASME international conference on advanced intelligent mechatronics, Wollongong, pp.1442–1447.
GruborJRandelSLangerK. (2008) Broadband information broadcasting using LED-based interior lighting. Lightwave Technology Journal26(24): 3883–3892.
11.
GustafssonF. (2003) Positioning using time different of arrival measurements. In: the IEEE international conference on acoustics, speech and signal proceedings, Hong Kong, vol. 6, pp.553–556.
12.
HsiehCHWangMLKaoLW. (2009) Mobile robot localization and path planning using an omnidirectional camera and infrared sensors. In: the 2009 IEEE international conference on systems, man, and cybernetics, Texas, pp.1947–1952.
13.
JiaSShengJShangE. (2012) Robot localization in indoor environments using radio frequency identification technology and stereo vision. Advanced Robotics Journal22(2–3): 279–297.
14.
JungSYChoiCKHeoSH. (2013) Received signal strength ratio based optical wireless indoor localization using light emitting diodes for illumination. In: the IEEE international conference on consumer electronics, Las Vegas, pp.63–64.
15.
KailathTSayedAHassibiB (2000) Linear Estimation. NJ: Prentice Hall publisher.
16.
KomineTNakagawaM (2004) Fundamental analysis for visible-light communication system using LED Lights. IEEE Transactions on Consumer Electronics50(1): 100–107.
17.
LarsenTDHansenKLAndersenNA. (1999) Design of Kalman filters for mobile robot, evaluation of kinematic and odometric approach. In: the 1999 IEEE international conference on control applications, Kohala Coast, vol.2, pp.1021–1026.
18.
LeeSSJungSY (2012) Location awareness using angle-of-arrival based circular-PD-array for visible light communication. In: the 18th Asia-Pacific conference on communications, Jeju, Korea, pp.480–485.
19.
NagaiINagataniKYamauchiG. (2013) Positioning device for outdoor mobile robots using optical sensors and lasers. Advanced Robotics Journal27(15): 1147–1160.
20.
NegenbornR (2003) Robot localization and Kalman filters on finding your position in a noisy world. Master’s Thesis, Utrecht University, Utrecht.
21.
PillonettoGErincGCarpinS (2012) Online estimation of covariance parameters using extended Kalman filtering and application to robot localization. Advanced Robotics Journal26(18): 2169–2188.
22.
RsticBArulampalamSGordonN (2004) Beyond the Kalman Filter: Particle Filters for Tracking Applications. Norwood, MA: Artech House Publisher.
23.
SantanaAMSousaAASBrittoRS. (2008) Localization of a mobile robot based in odometry and natural landmarks using Extended Kalman Filter. In: ICINCO-RA 2, INSTICC Press, pp.187–193.
24.
SiegwartRNourbakhshIRScaramuzzaD (2004) Introduction to Autonomous Mobile Robot. Cambridge, MA: MIT Press publisher.
25.
SimonD (2006) Optimal State Estimation: Kalman, H-infinity, and Nonlinear Approaches. Hoboken, NJ: John Wiley & Sons, Inc.
26.
ThrunS (2002) Particle filters in robotics. In: the eighteenth conference on uncertainty in artificial intelligence, San Francisco, pp.511–518.
27.
ThrunSBurgardWFoxD (2005) Probabilistic Robotics. Cambridge, MA: MIT Press.
28.
WangTQSekerciogluYANeildA. (2013) Position accuracy of time-of-arrival based ranging using visible light with application in indoor localization systems. Lightwave Technology Journal31(20): 3302–3308.
29.
WooJKimYJLeeJO. (2006) Localization of mobile robot using particle filter. In: the international joint conference on SICE-ICASE, Busan, pp.3031–3034.
30.
XuBYuRSunG. (2011) Whistle: synchronization-free TDOA for localization. In: the 31st international conference on distributed computing systems, Minnesota, pp.760–769.
