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Real-Time Stair Detection Using Multi-stage Ground Estimation Based on KMeans and RANSAC

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Trends and Applications in Information Systems and Technologies (WorldCIST 2021)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1365))

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Abstract

Multiplane estimation from three-dimensional (3D) point clouds is a necessary step in the negative obstacle detection. In recent years, different Random Sample Consensus (RANSAC) based methods have been proposed for this purpose. In this paper, we propose a multi-stage algorithm based on RANSAC plane estimation and KMeans clustering, and apply it to the negative stairs detection. This method contains two steps: first, it clusters the point clouds and downsamples them; second, it estimates the planes by iteratively using RANSAC algorithm with the downsampled data. Finally, according to the relationship between regions to determine whether there is an obstacle in front of the autonomous vehicle. Our experimental results show that this method has satisfactory performance.

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References

  1. Shang, E., An, X., Tao, W., Tingbo, H., Yuan, Q., He, H.: LiDAR based negative obstacle detection for field autonomous land vehicles. J. Field Robot. 33(5), 591–617 (2016)

    Article  Google Scholar 

  2. Gao, B., Xu, A., Pan, Y., Zhao, X., Yao, W., Zhao, H.: Off-road drivable area extraction using 3D LiDAR data. In: 2019 IEEE Intelligent Vehicles Symposium (IV) (2019)

    Google Scholar 

  3. Morton, R.D., Olson, E.: Positive and negative obstacle detection using the HLD classifier (2011)

    Google Scholar 

  4. Schnabel, R., Wahl, R., Klein, R.: Efficient RANSAC for point-cloud shape detection. Comput. Graph. Forum 26(2), 214–226 (2007)

    Article  Google Scholar 

  5. Saval-Calvo, M., Azorin-Lopez, J., Fuster-Guillo, A., Garcia-Rodriguez, J.: Three-dimensional planar model estimation using multi-constraint knowledge based on k-means and RANSAC. Appl. Soft Comput. 34, 572–586 (2015)

    Article  Google Scholar 

  6. Xu, G., Yuan, J., Li, X., Su, J.: Reconstruction method adopting laser plane generated from RANSAC and three dimensional reference. MAPAN-J. Metrol. Soc. India 33, 307–319 (2018)

    Google Scholar 

  7. Zheng, Y., Liu, J., Liu, Z., Wang, T., Ahmad, R.: A primitive-based 3D reconstruction method for remanufacturing. Int. J. Adv. Manuf. Technol. 103(9–12), 3667–3681 (2019)

    Article  Google Scholar 

  8. Muhovic, J., Mandeljc, R., Bovcon, B., Kristan, M., Pers, J.: Obstacle tracking for unmanned surface vessels using 3-D point cloud. IEEE J. Oceanic Eng. 45, 1–13 (2019)

    Google Scholar 

  9. Zhou, K., Richtsfeld, A., Varadarajan, K.M., Zillich, M., Vincze, M.: Combining plane estimation with shape detection for holistic scene understanding, pp. 736–747 (2011)

    Google Scholar 

  10. Qian, X., Ye, C.: NCC-RANSAC: a fast plane extraction method for 3-D range data segmentation. IEEE Trans. Cybern. 44(12), 2771–2783 (2014)

    Article  Google Scholar 

  11. Wang, W., Gao, W.: Efficient multi-plane extraction from massive 3D points for modeling large-scale urban scenes. Vis. Comput. 35(5), 625–638 (2019)

    Article  Google Scholar 

  12. Gallo, O., Manduchi, R., Rafii, A.: CC-RANSAC: fitting planes in the presence of multiple surfaces in range data. Pattern Recogn. Lett. 32(3), 403–410 (2011)

    Article  Google Scholar 

Download references

Acknowledgment

This work is financially supported by the Nature Science Foundation with No. 61862005, the Guangxi Nature Science Foundation with No. 2017GXNSFBA198226, the Scientific Research Foundation of Guangxi University with No. XGZ160483, the Higher Education Undergraduate Teaching Reform Project of Guangxi with No. 2017JGB108, and the project with No. DD3070051008.

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Authors and Affiliations

  1. School of Computer, Electronics and Information, Guangxi University, Nanning, 530004, People’s Republic of China

    Yuchen Li & Lina Yang

  2. Computer and Information Science, Northeastern University, Boston, MA, 02115, USA

    Patrick Shen-Pei Wang

Authors
  1. Yuchen Li
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  2. Lina Yang
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  3. Patrick Shen-Pei Wang
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Corresponding author

Correspondence to Lina Yang .

Editor information

Editors and Affiliations

  1. ISEG, University of Lisbon, Lisbon, Portugal

    Álvaro Rocha

  2. College of Engineering, The Ohio State University, Columbus, OH, USA

    Hojjat Adeli

  3. Institute of Data Science and Digital Technologies, Vilnius University, Vilnius, Lithuania

    Gintautas Dzemyda

  4. DCT, Universidade Portucalense, Porto, Portugal

    Fernando Moreira

  5. Department of Information Sciences, University of Sheffield, Lisbon, Portugal

    Ana Maria Ramalho Correia

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Li, Y., Yang, L., Wang, P.SP. (2021). Real-Time Stair Detection Using Multi-stage Ground Estimation Based on KMeans and RANSAC. In: Rocha, Á., Adeli, H., Dzemyda, G., Moreira, F., Ramalho Correia, A.M. (eds) Trends and Applications in Information Systems and Technologies. WorldCIST 2021. Advances in Intelligent Systems and Computing, vol 1365. Springer, Cham. https://doi.org/10.1007/978-3-030-72657-7_4

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