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CVC 2019: Advances in Computer Vision pp 458–472Cite as

Persistent Homology for Detection of Objects from Mobile LiDAR Point Cloud Data in Autonomous Vehicles

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 944))

Abstract

Recently, researchers have paid significant attention to problems related to object detection and computer vision for autonomous vehicles. Such vehicles offer many benefits, including their ability to help address transportation-related issues such as safety concerns, traffic jams, and overall mobility. Multi-beam ‘light detection and ranging’ (LiDAR) is one of the main sensors that is used to sense and detect objects by creating a point cloud data map of the surrounding environment. Current object detection tasks that use only mobile LiDAR data divide the entire area into cubes and employ image object detection methods. Such an approach poses challenges due to the third dimension that increases computational time, which thus requires a tradeoff between performance and time optimization. In this paper, we propose a new approach to detect objects using point cloud data by investigating the shapes of the objects. To this end, we developed a method based on topological data analysis achieved via persistent homology to analyze the qualitative properties of the data. To the best of our knowledge, our work is the first to develop topological data analysis for real-world mobile LiDAR point cloud data exploration. The evaluation result shows a high accuracy classification result using features extracted from barcodes.

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References

  1. Fagnant, D.J., Kockelman, K.: Preparing a nation for autonomous vehicles: opportunities, barriers and policy recommendations. Transp. Res. Part A: Policy Pract. 77, 167–181 (2015)

    Google Scholar 

  2. Howard, D., Dai, D.: Public perceptions of self-driving cars: the case of Berkeley, California. In: Transportation Research Board 93rd Annual Meeting, vol. 14, no. 4502 (2014)

    Google Scholar 

  3. Liu, S., Li, L., Tang, J., Wu, S., Gaudiot, J.L.: Creating autonomous vehicle systems. Synth. Lect. Comput. Sci. 6(1), i–186 (2017)

    Google Scholar 

  4. Velodyne Lidar Price Reduction – Self-Driving Cars – Medium. https://medium.com/self-driving-cars/velodyne-lidarprice-reduction-d358f245f086. Accessed 14 Apr 2018

  5. Huang, J., You, S.: Detecting objects in scene point cloud: a combinational approach. In: 3D Vision-3DV 2013, pp. 175–182. IEEE (2013)

    Google Scholar 

  6. Geiger, A., Lenz, P., Stiller, C., Urtasun, R.: The KITTI vision benchmark suite (2015)

    Google Scholar 

  7. Ghrist, R.: Homological algebra and data. preprint (2017)

    Google Scholar 

  8. Zomorodian, A., Carlsson, G.: Computing persistent homology. Discrete Comput. Geom. 33(2), 249–274 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  9. Collins, A., Zomorodian, A., Carlsson, G., Guibas, L.J.: A barcode shape descriptor for curve point cloud data. Comput. Graph. 28(6), 881–894 (2004)

    Article  Google Scholar 

  10. Edelsbrunner, H., Letscher, D., Zomorodian, A.: Topological persistence and simplification. In: Proceedings of the 41st Annual Symposium on Foundations of Computer Science, pp. 454–463. IEEE (2000)

    Google Scholar 

  11. Bubenik, P.: Statistical topological data analysis using persistence landscapes. J. Mach. Learn. Res. 16(1), 77–102 (2015)

    MathSciNet  MATH  Google Scholar 

  12. Schwarz, B.: LIDAR: mapping the world in 3D. Nat. Photonics 4(7), 429 (2010)

    Article  Google Scholar 

  13. Himmelsbach, M., Hundelshausen, F.V., Wuensche, H.J.: Fast segmentation of 3D point clouds for ground vehicles. In: Intelligent Vehicles Symposium (IV), pp. 560–565. IEEE (2010)

    Google Scholar 

  14. Douillard, B., Underwood, J., Kuntz, N., Vlaskine, V., Quadros, A., Morton, P., Frenkel, A.: On the segmentation of 3D LIDAR point clouds. In: 2011 IEEE International Conference on Robotics and Automation, ICRA, pp. 2798–2805. IEEE (2011)

    Google Scholar 

  15. Wang, D.Z., Posner, I., Newman, P.: What could move? Finding cars, pedestrians and bicyclists in 3D laser data. In: 2012 IEEE International Conference on Robotics and Automation, ICRA, pp. 4038–4044. IEEE (2012)

    Google Scholar 

  16. Behley, J., Steinhage, V., Cremers, A.B.: Laser-based segment classification using a mixture of bag-of-words. In: 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS, pp. 4195–4200. IEEE (2013)

    Google Scholar 

  17. Wang, D.Z., Posner, I.: Voting for voting in online point cloud object detection. In: Robotics: Science and Systems, vol. 1, p. 5 (2015)

    Google Scholar 

  18. Li, B., Zhang, T., Xia, T.: Vehicle detection from 3D lidar using fully convolutional network. arXiv preprint arXiv:1608.07916 (2016)

  19. Maturana, D., Scherer, S.: 3D convolutional neural networks for landing zone detection from lidar. In: 2015 IEEE International Conference on Robotics and Automation, ICRA, pp. 3471–3478. IEEE (2015)

    Google Scholar 

  20. Graham, B.: Sparse 3D convolutional neural networks. arXiv preprint arXiv:1505.02890 (2015)

  21. Zhou, Y., Tuzel, O.: VoxelNet: end-to-end learning for point cloud based 3D object detection. arXiv preprint arXiv:1711.06396 (2017)

  22. Li, C., Ovsjanikov, M., Chazal, F.: Persistence-based structural recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1995–2002. IEEE (2014)

    Google Scholar 

  23. Reininghaus, J., Huber, S., Bauer, U., Kwitt, R.: A stable multi-scale kernel for topological machine learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4741–4748. IEEE (2015)

    Google Scholar 

  24. Zeppelzauer, M., Zieliński, B., Juda, M., Seidl, M.: Topological descriptors for 3D surface analysis. In: International Workshop on Computational Topology in Image Context, pp. 77–87. Springer, Cham (2016)

    Google Scholar 

  25. Thrun, S., Montemerlo, M., Dahlkamp, H., Stavens, D., Aron, A., Diebel, J., Fong, P., Gale, J., Halpenny, M., Hoffmann, G., Lau, K.: Stanley: the robot that won the DARPA Grand Challenge. J. Field Robot. 23(9), 661–692 (2006)

    Article  Google Scholar 

  26. Zhang, M., Morris, D.D., Fu, R.: Ground segmentation based on loopy belief propagation for sparse 3D point clouds. In: 2015 International Conference on 3D Vision, 3DV, pp. 615–622. IEEE (2015)

    Google Scholar 

  27. Velas, M., Spanel, M., Hradis, M., Herout, A.: CNN for very fast ground segmentation in Velodyne lidar data. arXiv preprint arXiv:1709.02128 (2017)

  28. Ester, M., Kriegel, H. P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: KDD, vol. 96, no. 34, pp. 226–231 (1996)

    Google Scholar 

  29. Otter, N., Porter, M.A., Tillmann, U., Grindrod, P., Harrington, H.A.: A roadmap for the computation of persistent homology. EPJ Data Sci. 6(1), 17 (2017)

    Article  Google Scholar 

  30. Pereira, C.M., de Mello, R.F.: Persistent homology for time series and spatial data clustering. Expert Syst. Appl. 42(15–16), 6026–6038 (2015)

    Article  Google Scholar 

  31. Adcock, A., Carlsson, E., Carlsson, G.: The ring of algebraic functions on persistence bar codes. arXiv preprint arXiv:1304.0530 (2013)

  32. Giansiracusa, N., Giansiracusa, R., Moon, C.: Persistent homology machine learning for fingerprint classification. arXiv preprint arXiv:1711.09158 (2017)

  33. Pandey, G., McBride, J.R., Eustice, R.M.: Ford campus vision and lidar data set. Int. J. Robot. Res. 30(13), 1543–1552 (2011)

    Article  Google Scholar 

  34. Badino, H., Huber, D., Kanade, T.: The CMU visual localization data set. Computer Vision Group (2011)

    Google Scholar 

  35. Fasy, B. T., Kim, J., Lecci, F., Maria, C.: Introduction to the R package TDA. arXiv preprint arXiv:1411.1830 (2014)

  36. Everingham, M., Van Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The Pascal visual object classes (VOC) challenge. Int. J. Comput. Vis. 88(2), 303–338 (2010)

    Article  Google Scholar 

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Authors and Affiliations

  1. Geoinformatics Laboratory, School of Computing and Information, University of Pittsburgh, 135 North Bellefield Avenue, Pittsburgh, PA, USA

    Meirman Syzdykbayev & Hassan A. Karimi

Authors
  1. Meirman Syzdykbayev
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  2. Hassan A. Karimi
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Corresponding authors

Correspondence to Meirman Syzdykbayev or Hassan A. Karimi .

Editor information

Editors and Affiliations

  1. Saga University, Saga, Saga, Japan

    Kohei Arai

  2. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Supriya Kapoor

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Syzdykbayev, M., Karimi, H.A. (2020). Persistent Homology for Detection of Objects from Mobile LiDAR Point Cloud Data in Autonomous Vehicles. In: Arai, K., Kapoor, S. (eds) Advances in Computer Vision. CVC 2019. Advances in Intelligent Systems and Computing, vol 944. Springer, Cham. https://doi.org/10.1007/978-3-030-17798-0_37

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