Accurate Localization in Urban Environments Using Fault Detection of GPS and Multi-sensor Fusion

  • Taekjun OhEmail author
  • Myung Jin Chung
  • Hyun Myung
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 447)


In order to make robots perform tasks autonomously, it is necessary for robots to know the surrounding environments. Therefore, a world modeling should be made in advance or concurrently. It is important to know an accurate position for the accurate world modeling. The aim of this paper is an accurate localization method for the world modeling under the situation where the portion of signals from global positioning system (GPS) satellites is blocked in urban environments. In this paper, we propose a detection method for non-line-of-sight satellites and a localization method using the GPS, the inertial measurement unit (IMU), the wheel encoder, and the laser range finder (LRF). To decide whether the signal from the satellite is blocked by the building, the local map that is made from the local sensors and an LRF is exploited. Then the GPS reliability is established adaptively in a non-line-of-sight situation. Through an extended Kalman filter (EKF) with the GPS reliability the final robot pose is estimated. To evaluate the performance of the proposed methods, the accuracy of the proposed method is analyzed using ground truth from Google maps. Experimental results demonstrate that the proposed method is suitable for the urban environments.


Multi-sensor Localization World modeling Non-line-of-sight 



This work was financially supported by the Korean Ministry of Land, Infrastructure and Transport (MOLIT) as 「U-City Master and Doctor Course Grant Program」


  1. 1.
    Montemerlo, M., Thrun, S., Koller, D., Wegbreit, B.: FastSLAM: a factored solution to the simultaneous localization and mapping problem. In: AAAI/IAAI, pp. 593–598 (2002)Google Scholar
  2. 2.
    Grisetti, G., Stachniss, C., Burgard, W.: Improving grid-based SLAM with Rao-blackwellized particle filters by adaptive proposals and selective resampling. In: IEEE International Conference on Robotics and Automation, pp. 2432–2437. IEEE Press, New York (2005)Google Scholar
  3. 3.
    Dellaert, F., Kaess, M.: Square root SAM: simultaneous localization and mapping via square root information smoothing. Int. J. Robot. Res. 25(12), 1181–1203 (2006)CrossRefzbMATHGoogle Scholar
  4. 4.
    Kaess, M., Johannsson, H., Roberts, R., Ila, V., Leonard, J.J., Dellaert, F.: iSAM2: incremental smoothing and mapping using the bayes tree. Int. J. Robot. Res. 31(2), 217–236 (2011)Google Scholar
  5. 5.
    Kim, H., Oh, T., Lee, D., Choe, Y., Chung, M.J., Myung, H.: Mobile robot localization by matching 2D image features to 3D point cloud. In: Ubiquitous Robots and Ambient Intelligence (2013)Google Scholar
  6. 6.
    Kim, H., Lee, D., Oh, T., Lee, S.W., Choe, Y., Myung, H.: Feature-based 6-DoF camera localization using prior point cloud and images. In: Robot Intelligence Technology and Applications, pp. 3–11. Springer International Publishing (2014)Google Scholar
  7. 7.
    Lee, D., Jung, J., and Myung, H.: Pose graph-based RGB-D SLAM in low dynamic environments. In: IEEE International Conference on Robotics and Automation. IEEE Press, New York (2014)Google Scholar
  8. 8.
    Lee, D., Myung, H.: Solution to the SLAM problem in low dynamic environments using a pose graph and an RGB-D sensor. Sensors 14(7), 12467–12496 (2014)CrossRefGoogle Scholar
  9. 9.
    Baldwin, I., Newman, P.: Road vehicle localization with 2D push-broom LIDAR and 3D priors. In: IEEE International Conference on Robotics and Automation, pp. 2611–2617. IEEE Press, New York (2012)Google Scholar
  10. 10.
    Qin, B., chong, Z.J., Bandyopadhyay, T., Ang. Jr. M. H., Frazzoli, E., Rus, D.: Curb-intersection feature based monte carlo localization on Uban roads. In: IEEE International Conference on Robotics and Automation, pp. 2640–2646. IEEE Press, New York (2012)Google Scholar
  11. 11.
    Stewart, D., Newman, P.: LAPS-localization using appearance of prior structure: 6-DoF monocular camera localisation using prior pointclouds. In: IEEE International Conference on Robotics and Automation, pp. 2625–2632. IEEE Press, New York (2012)Google Scholar
  12. 12.
    Senlet, T., Elgammal, A.: Satellite image based precise robot localization on sidewalks. In: IEEE International Conference on Robotics and Automation, pp. 2647–2653. IEEE Press, New York (2012)Google Scholar
  13. 13.
    Obst, M., Bauer, S., Reisdorf, P., Wanielik, G.: Multipath detection with 3D digital maps for robust multi-constellation GNSS/INS vehicle localization in urban areas. In: IEEE Intelligent Vehicles Symposium, pp. 184–190. IEEE Press, New York (2012)Google Scholar
  14. 14.
    Meguro, J.-I., Murata, T., Takiguchi, J.-I., Amano, Y., Hashizume, T.: GPS multipath mitigation for urban area using omnidirectional infrared camera. IEEE Trans. Intell. Transport. Syst. 10.1, 22–30 (2009)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  1. 1.Department of Civil and Environmental EngineeringKAISTDaejeonKorea
  2. 2.Department of Electrical EngineeringKAISTDaejeonKorea

Personalised recommendations