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3D object recognition method with multiple feature extraction from LiDAR point clouds

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Abstract

During autonomous driving, fast and accurate object recognition supports environment perception for local path planning of unmanned ground vehicles. Feature extraction and object recognition from large-scale 3D point clouds incur massive computational and time costs. To implement fast environment perception, this paper proposes a 3D recognition system with multiple feature extraction from light detection and ranging point clouds modified by parallel computing. Effective object feature extraction is a necessary step prior to executing an object recognition procedure. In the proposed system, multiple geometry features of a point cloud that resides in corresponding voxels are computed concurrently. In addition, a scale filter is employed to convert feature vectors from uncertain count voxels to a normalized object feature matrix, which is convenient for object-recognizing classifiers. After generating the object feature matrices of all voxels, an initialized multilayer neural network (NN) model is trained offline through a large number of iterations. Using the trained NN model, real-time object recognition is realized using parallel computing technology to accelerate computation.

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References

  1. Yao J, Zhang K, Yang Y et al (2018) Emergency vehicle route oriented signal coordinated control model with two-level programming. Soft Comput 2(13):4283–4294

    Article  Google Scholar 

  2. Simony M, Milzy S, Amende K et al (2018) Complex-YOLO: an Euler-region-proposal for real-time 3D object detection on point clouds. In: Computer Vision-ECCV 2018 Workshops, pp 197–209

  3. Chen X, Ma H, Wan J et al (2017) Multi-view 3D object detection network for autonomous driving. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 1907–1915

  4. Guo Y, Bennamoun M, Sohel F et al (2014) 3D object recognition in cluttered scenes with local surface features: a survey. IEEE Trans Pattern Anal Mach Intell 36(11):2270–2287

    Article  Google Scholar 

  5. Stamatis OK, Aouf N, Gray G et al (2018) Local feature based automatic target recognition for future 3D active homing seeker missiles. Aerosp Sci Technol 73:309–317

    Article  Google Scholar 

  6. Zeng H, Wang H, Dong J (2017) Robust 3D keypoint detection method based on double Gaussian weighted dissimilarity measure. Multimed Tools Appl 76(24):26377–26389

    Article  Google Scholar 

  7. Wang J, Lindenbergh R, Menenti M (2017) SigVox—a 3D feature matching algorithm for automatic street object recognition in mobile laser scanning point clouds. ISPRS J Photogramm Remote Sens 128:111–129

    Article  Google Scholar 

  8. Zeng H, Liu Y, Liu J et al (2018) Non-rigid 3D model retrieval based on quadruplet convolutional neural networks. IEEE Access 6:76087–76097

    Article  Google Scholar 

  9. Watanabe T, Yamazaki K, Yokokohji Y (2017) Survey of robotic manipulation studies intending practical applications in real environments-object recognition, soft robot hand, and challenge program and benchmarking. Adv Robot 31(19–20):1114–1132

    Article  Google Scholar 

  10. Li L, Ota K, Dong M (2018) Humanlike driving: empirical decision-making system for autonomous vehicles. IEEE Trans Veh Technol 67(8):6814–6823

    Article  Google Scholar 

  11. Buenoa M, González-Jorgea H, Sánchez JM et al (2017) Automatic point cloud coarse registration using geometric keypoint descriptors for indoor scenes. Autom Constr 81:134–148

    Article  Google Scholar 

  12. Sun J, Zhang J, Zhang G (2016) An automatic 3D point cloud registration method based on regional curvature maps. Image Vis Comput 56:49–58

    Article  Google Scholar 

  13. Persad RA, Armenakis C (2017) Automatic co-registration of 3D multi-sensor point clouds. ISPRS J Photogramm Remote Sens 130:162–186

    Article  Google Scholar 

  14. Ge X (2017) Automatic markerless registration of point clouds with semantic-keypoint-based 4-points congruent sets. ISPRS J Photogramm Remote Sens 130:344–357

    Article  Google Scholar 

  15. Hansch R, Webera T, Hellwich O (2014) Comparison of 3D interest point detectors and descriptors for point cloud fusion. ISPRS Ann Photogramm Remote Sens Spat Inf Sci II-3:57–64

    Article  Google Scholar 

  16. Weber T, Hänsch R, Hellwich O (2015) Automatic registration of unordered point clouds acquired by Kinect sensors using an overlap heuristic. ISPRS J Photogramm Remote Sens 102:96–109

    Article  Google Scholar 

  17. Yang J, Zhang Q, Cao Z (2017) The effect of spatial information characterization on 3D local feature descriptors: a quantitative evaluation. Pattern Recogn 66:375–391

    Article  Google Scholar 

  18. Yang J, Cao Z, Zhang Q (2016) A fast and robust local descriptor for 3D point cloud registration. Inf Sci 346–347:163–179

    Article  Google Scholar 

  19. Garcia AG, Escolano SO, Rodriguez JG et al (2018) Interactive 3D object recognition pipeline on mobile GPGPU computing platforms using low-cost RGB-D sensors. J Real-Time Image Proc 14:585–604

    Article  Google Scholar 

  20. Quan S, Ma J, Hu F et al (2018) Local voxelized structure for 3D binary feature representation and robust registration of point clouds from low-cost sensors. Inf Sci 444:153–171

    Article  Google Scholar 

  21. Zhu Q, Li Y, Hu H et al (2017) Robust point cloud classification based on multi-level semantic relationships for urban scenes. ISPRS J Photogramm Remote Sens 129:86–102

    Article  Google Scholar 

  22. Xu Y, Tuttas S, Hoegner L et al (2018) Voxel-based segmentation of 3D point clouds from construction sites using a probabilistic connectivity model. Pattern Recogn Lett 102:67–74

    Article  Google Scholar 

  23. Yang B, Dong Z, Zhao G et al (2015) Hierarchical extraction of urban objects from mobile laser scanning data. ISPRS J Photogramm Remote Sens 99:45–57

    Article  Google Scholar 

  24. Lei H, Jiang G, Quan L (2017) Fast descriptors and correspondence propagation for robust global point cloud registration. IEEE Trans Image Process 26(8):3614–3623

    MathSciNet  MATH  Google Scholar 

  25. Elbaz G, Avraham T, Fischer A (2017) 3D point cloud registration for localization using a deep neural network auto-encoder. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 4631–4640

  26. Ligon J, Bein D, Ly P et al (2018) 3D point cloud processing using spin images for object detection. In: 2018 IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC), pp 731–736

  27. Yang J, Zhang Q, Xian K et al (2017) Rotational contour signatures for both real-valued and binary feature representations of 3D local shape. Comput Vis Image Underst 160:133–147

    Article  Google Scholar 

  28. Dong Z, Yang B, Liu Y et al (2017) A novel binary shape context for 3D local surface description. ISPRS J Photogramm Remote Sens 130:431–452

    Article  Google Scholar 

  29. Tu Y, Lin Y, Wang J et al (2018) Semi-supervised learning with generative adversarial networks on digital signal modulation classification. Comput Mater Contin 55(2):243–254

    Google Scholar 

  30. Zeng D, Dai Y, Li F et al (2018) Adversarial learning for distant supervised relation extraction. Comput Mater Contin 55(1):121–136

    Google Scholar 

  31. Dubé R, Gollub MG, Sommer H et al (2018) Incremental-segment-based localization in 3-D point clouds. IEEE Robot Autom Lett 3(3):1832–1839

    Article  Google Scholar 

  32. Soilán M, Riveiro B, Sánchez JM et al (2017) Segmentation and classification of road markings using MLS data. ISPRS J Photogramm Remote Sens 123:94–103

    Article  Google Scholar 

  33. Roveri R, Rahmann L, Oztireli AC et al (2018) A network architecture for point cloud classification via automatic depth images generation. In: IEEE Conference on Computer Vision Pattern Recognition (CVPR), pp 4176–4184

  34. Bobkov D, Chen S, Jian R et al (2018) Noise-resistant deep learning for object classification in three-dimensional point clouds using a point pair descriptor. IEEE Robot Autom Lett 3(2):865–872

    Article  Google Scholar 

  35. Chen J, Cho YK, Ueda J (2018) Sampled-point network for classification of deformed building element point clouds. In: 2018 IEEE International Conference on Robotics and Automation (ICRA), pp 2164–2169

  36. Song W, Tian Y, Fong S et al (2016) GPU-accelerated foreground segmentation and labeling for real-time video surveillance. Sustainability 8(10):916

    Article  Google Scholar 

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Acknowledgements

This research was supported by National Natural Science Foundation of China (61503005), Beijing Natural Science Foundation (4184086), Beijing Young Topnotch Talents Cultivation Program (No. CIT&TCD201904009), the Great Wall Scholar Program (CIT&TCD20190304), NCUT “The Belt and Road” Talent Training Base Project, and NCUT “Yuyou” Project.

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Authors and Affiliations

  1. North China University of Technology, No. 5 Jinyuanzhuang Road, Shijingshan District, Beijing, 100-144, China

    Yifei Tian, Wei Song, Su Sun & Shuanghui Zou

  2. Department of Computer and Information Science, University of Macau, Taipa, 999-078, Macau, China

    Yifei Tian & Simon Fong

  3. Beijing Key Lab on Urban Intelligent Traffic Control Technology, Beijing, 100-144, China

    Wei Song

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  1. Yifei Tian
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  2. Wei Song
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  5. Shuanghui Zou
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Correspondence to Wei Song.

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Tian, Y., Song, W., Sun, S. et al. 3D object recognition method with multiple feature extraction from LiDAR point clouds. J Supercomput 75, 4430–4442 (2019). https://doi.org/10.1007/s11227-019-02830-9

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  • DOI: https://doi.org/10.1007/s11227-019-02830-9

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