Skip to main content
SpringerLink
Log in
Book cover

Robot Intelligence Technology and Applications 4 pp 489–500Cite as

A Likelihood-Based Data Fusion Model for the Integration of Multiple Sensor Data: A Case Study with Vision and Lidar Sensors

  • Conference paper
  • First Online:

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 447))

Abstract

Sensors have been developed and applied in a wide range of fields such as robotics and autonomous vehicle navigation (AVN). Due to the inability of a single sensor to fully sense its surroundings, multiple sensors based on individual specialties are commonly used in order to complement the shortcomings and enrich perception. However, it is challenging to integrate the heterogeneous types of sensory information and produce useful results. This research aims to achieve a high degree of accuracy with a minimum false-positive and false-negative rate for the sake of reliability and safety. This paper introduces a likelihood-based data fusion model, which integrates information from various sensors, maps it into the integrated data space and generates the solution considering all the information from the sensors. Two distinct sensors: an optical camera and a LIght Detection And Range (Lidar) sensor were used for the experiment. The experimental results showed the usefulness of the proposed model in comparison with single sensor outcomes.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Khaleghi, B., Khamis, A., Karray, F.O., Razavi, S.N.: Multisensor data fusion: a review of the state-of-the-art. Inf. Fusion (2011)

    Google Scholar 

  2. How, J.P., Bethke, B., Frank, A., Dale, D.,Vian, J.: Real-time indoor autonomous vehicle test environment. IEEE Control Syst. Mag. 51–64 (2008)

    Google Scholar 

  3. Subramaniana, V., Burksa, T.F., Arroyob, A.A.: Development of machine vision and laser radar based autonomous vehicle guidance systems for citrus grove navigation. Comput. Electron. Agric. 53(2), 130–143 (2006)

    Article  Google Scholar 

  4. Sun, J., Wu, Z., Pan, G.: Context-aware smart vehicle: from model to prototype. J. Zhejiang Univ. Sci. A 10(7), 1049–1059 (2009)

    Article  Google Scholar 

  5. Hillel, A.B., Lerner, R., Levi, D., Raz, G.: Recent progress in road and lane detection: a survey. Mach. Vis. Appl. 1–19 (2012)

    Google Scholar 

  6. Huang, A.S., Moore, D., Antone, M., Olson, E., Teller, S.: Finding multiple lanes in urban road networks with vision and lidar. Auton. Robots 26(2–3), 103–122 (2009)

    Article  Google Scholar 

  7. Cheng, Y.: Mean shift, mode seeking, and clustering. IEEE Trans. Pattern Anal. Mach. Intell. 17(8), 790–799 (1995)

    Article  Google Scholar 

  8. Kodagoda, K., Ge, S.S., Wijesoma, W.S., Balasuriya, A.P.: IMMPDAF approach for road-boundary tracking. IEEE Trans. Veh. Technol. 56(2), 478–486 (2007)

    Article  Google Scholar 

  9. Garcia, F., Musleh, B., de la Escalera, A., Armingol, J.M.: Fusion procedure for pedestrian detection based on laser scanner and computer vision. In: 2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC), pp. 1325–1330 (2011)

    Google Scholar 

  10. Morrison, R.: Fiducial marker detection and pose estimation from lidar range data. Technical Report. DTIC Document (2010)

    Google Scholar 

  11. Adhikari, S.P., Kim, H.: Dynamic programming and curve fitting based road boundary detection. In: 9th WSEAS International Conference on Computational Intelligence, pp. 236–240 (2010)

    Google Scholar 

  12. Jo, J.H., Tsunoda, Y., Sullivan, T., Lennon, M., Jo, T., Chun, Y.: BINS: blackboard-based intelligent navigation system for multiple sensory data integration. In: The 17th International Conference on Image Processing, Computer Vision, and Pattern Recognition, Las Vegas, USA (2013)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Information and Communication Technology, Griffith University, Nathan, Australia

    Jun Jo, Yukito Tsunoda, Bela Stantic & Alan Wee-Chung Liew

Authors
  1. Jun Jo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yukito Tsunoda
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bela Stantic
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Alan Wee-Chung Liew
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jun Jo .

Editor information

Editors and Affiliations

  1. Electrical Engineering, Korea Advanced Institute of Science & Technology, Daejeon, Korea (Republic of)

    Jong-Hwan Kim

  2. Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, Ontario, Canada

    Fakhri Karray

  3. School of Information and Communication Technology, Griffith University, Gold Coast, Queensland, Australia

    Jun Jo

  4. Center for Intelligent Technologies, Technical University of Kosice, Kosice, Slovakia

    Peter Sincak

  5. Civil and Environmental Engineering, Korea Advanced Institute of Science & Technology, Daejeon, Korea (Republic of)

    Hyun Myung

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing Switzerland

About this paper

Cite this paper

Jo, J., Tsunoda, Y., Stantic, B., Liew, A.WC. (2017). A Likelihood-Based Data Fusion Model for the Integration of Multiple Sensor Data: A Case Study with Vision and Lidar Sensors. In: Kim, JH., Karray, F., Jo, J., Sincak, P., Myung, H. (eds) Robot Intelligence Technology and Applications 4. Advances in Intelligent Systems and Computing, vol 447. Springer, Cham. https://doi.org/10.1007/978-3-319-31293-4_39

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-31293-4_39

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-31291-0

  • Online ISBN: 978-3-319-31293-4

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation