Tracking a Mobile Robot Position Using Vision and Inertial Sensor

  • Francisco Coito
  • António Eleutério
  • Stanimir Valtchev
  • Fernando Coito
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 423)

Abstract

Wheeled mobile robots are still the first choice when it comes to industrial or domotic applications. The robot’s navigation system aims to reliably determine the robot’s position, velocity and orientation and provide it to control and trajectory guidance modules. The most frequently used sensors are inertial measurement units (IMU) combined with an absolute position sensing mechanism. The dead reckoning approach using IMU suffers from integration drift due to noise and bias. To overcome this limitation we propose the use of the inertial system in combination with mechanical odometers and a vision based system. These two sensor complement each other as the vision sensor is accurate at low-velocities but requires long computation time, while the inertial sensor is able to track fast movements but suffers from drift. The information from the sensors is integrated through a multi-rate fusion scheme. Each of the sensor systems is assumed to have it’s own independent sampling rate, which may be time-varying. Data fusion is performed by a multi-rate Kalman filter. The paper describes the inertial and vision navigation systems, and the data fusion algorithm. Simulation and experimental results are presented.

Keywords

Mobile robotics multi-rate sampling sensor fusion vision inertial sensor 

References

  1. 1.
    Kim, S.J., Kim, B.K.: Dynamic Ultrasonic Hybrid Localization System for Indoor Mobile Robots. IEEE Trans. Ind. Elect. 60(10), 4562–4573 (2013)CrossRefGoogle Scholar
  2. 2.
    Lee, T., Shirr, J., Cho, D.: Position Estimation for Mobile Robot Using In-plane 3-Axis IMU and Active Beacon. In: IEEE Int. Symp. Ind Electronics, pp. 1956–1961. IEEE Press (2009)Google Scholar
  3. 3.
    Tennina, S., Valletta, M., Santucci, F., Di Renzo, M., Graziosi, F.: Minutolo.: Entity Localization and Tracking: A Sensor Fusion-based Mechanism in WSNs. In: IEEE 13th Int Conf. on Digital Object Identifier, pp. 983–988. IEEE Press, New York (2011)Google Scholar
  4. 4.
    Armesto, L., Chroust, S., Vincze, M., Tornero, J.: Multi-rate fusion with vision and inertial sensors. In: IEEE Int. Conf. on Robotics and Automation, pp. 193–199. IEEE Press (2004)Google Scholar
  5. 5.
    Bancroft, J.B., Lachapelle, G.: Data Fusion Algorithms for Multiple Inertial Measurement Units. Sensors 11(7), 6771–6798 (2011)CrossRefGoogle Scholar
  6. 6.
    Hol, J., Schön, T., Luinge, H., Slycke, P., Gustafsson, F.: Robust real-time tracking by fusing measurements from inertial and vision sensors. J. Real-Time Image Proc. 2(3), 149–160 (2007)CrossRefGoogle Scholar
  7. 7.
    Cho, B.-S., Moon, W.-S., Seo, O.-J., Baek, K.-R.: A dead reckoning localization system for mobile robots using inertial sensors and wheel revolution encoding. Sensors 11(7), 6771–6798 (2011), J. Mech. Sc. Tech. 25-11, pp. 2907-2917 (2011)Google Scholar
  8. 8.
    Marín, L., Vallés, M., Soriano, Á., Valera, Á., Albertos, P.: Multi Sensor Fusion Framework for Indoor-Outdoor Localization of Limited Resource Mobile Robots. Sensors 13(10), 14133–14160 (2013)CrossRefGoogle Scholar
  9. 9.
    Rosten, E., Porter, R., Drummond, T.: Faster and Better: A Machine Learning Approach to Corner Detection. IEEE Tans. Pattern Anal. Mach. Int. 32(1), 105–1019 (2010)CrossRefGoogle Scholar
  10. 10.
    Lucas, A., Christo, C., Silva, M.P., Cardeira, C.: Mosaic based flexible navigation for AGVs. In: IEEE Int Symp. Industrial Electronics, pp. 3545–3550. IEEE Press (2010)Google Scholar
  11. 11.
    Hu, Y., Duan, Z., Zhou, D.: Estimation Fusion with General Asynchronous Multi-Rate Sensors. IEEE Trans. Aerosp. El. Sys. 46(4), 2090–2102 (2010)CrossRefGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2014

Authors and Affiliations

  • Francisco Coito
    • 1
  • António Eleutério
    • 1
  • Stanimir Valtchev
    • 1
  • Fernando Coito
    • 1
  1. 1.Faculdade de Ciências e TecnologiaUniversidade Nova de LisboaCaparicaPortugal

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