Document Open Access Logo

TrueView: A LIDAR Only Perception System for Autonomous Vehicle (Interactive Presentation)

Authors Mohammed Yazid Lachachi, Mohamed Ouslim, Smail Niar, Abdelmalik Taleb-Ahmed

PDF
Thumbnail PDF

File

OASIcs.ASD.2019.8.pdf
  • Filesize: 2.77 MB
  • 10 pages

Document Identifiers

Author Details

Mohammed Yazid Lachachi
  • University of sciences and technologies d'Oran - Mohamed-Boudiaf, LMSE, Algeria
Mohamed Ouslim
  • University of sciences and technologies d'Oran - Mohamed-Boudiaf, LMSE, Algeria
Smail Niar
  • Université Polytechnique Hauts-de-France, France
Abdelmalik Taleb-Ahmed
  • Université Polytechnique Hauts-de-France, France

Cite As Get BibTex

Mohammed Yazid Lachachi, Mohamed Ouslim, Smail Niar, and Abdelmalik Taleb-Ahmed. TrueView: A LIDAR Only Perception System for Autonomous Vehicle (Interactive Presentation). In Workshop on Autonomous Systems Design (ASD 2019). Open Access Series in Informatics (OASIcs), Volume 68, pp. 8:1-8:10, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019) https://doi.org/10.4230/OASIcs.ASD.2019.8

Abstract

Real time perception and understanding of the environment is essential for an autonomous vehicle. To obtain the most accurate perception, existing solutions propose to combine multiple sensors. However, a large number of embedded sensors in the vehicle implies to process a large amount of data thus increasing the system complexity and cost. In this work, we present a novel approach that uses only one LIDAR sensor. Our approach enables reducing the size and complexity of the used machine learning algorithm. A novel approach is proposed to generate multiple 2D representation from 3D points cloud using the LIDAR sensor. The obtained representation solves the sparsity and connectivity issues encountered with LIDAR-based solution.

Subject Classification

ACM Subject Classification
  • Computing methodologies → Computer vision representations
Keywords
  • Ranging Data
  • Computer Vision
  • Machine Learning

Metrics

  • Access Statistics
  • Total Accesses (updated on a weekly basis)
    0
    PDF Downloads
    0
    Metadata Views

References

  1. Henrik Andreasson, Rudolph Triebel, and Achim Lilienthal. Non-iterative vision-based interpolation of 3D laser scans. In Autonomous Robots and Agents, pages 83-90. Springer, 2007. Google Scholar
  2. Florian Chabot, Mohamed Chaouch, Jaonary Rabarisoa, Céline Teulière, and Thierry Chateau. Deep MANTA: A Coarse-to-fine Many-Task Network for joint 2D and 3D vehicle analysis from monocular image. In Proc. IEEE Conf. Comput. Vis. Pattern Recognit.(CVPR), pages 2040-2049, 2017. Google Scholar
  3. Zhe Chen and Zijing Chen. RBNet: A Deep Neural Network for Unified Road and Road Boundary Detection. In International Conference on Neural Information Processing, pages 677-687. Springer, 2017. Google Scholar
  4. James Diebel and Sebastian Thrun. An application of markov random fields to range sensing. In Advances in neural information processing systems, pages 291-298, 2006. Google Scholar
  5. Martin Dimitrievski, Peter Veelaert, and Wilfried Philips. Learning Morphological Operators for Depth Completion. In International Conference on Advanced Concepts for Intelligent Vision Systems, pages 450-461. Springer, 2018. Google Scholar
  6. Rex A Dwyer. A faster divide-and-conquer algorithm for constructing Delaunay triangulations. Algorithmica, 2(1-4):137-151, 1987. Google Scholar
  7. Herbert Edelsbrunner and Nimish R Shah. Incremental topological flipping works for regular triangulations. Algorithmica, 15(3):223-241, 1996. Google Scholar
  8. Leonidas J Guibas, Donald E Knuth, and Micha Sharir. Randomized incremental construction of Delaunay and Voronoi diagrams. Algorithmica, 7(1-6):381-413, 1992. Google Scholar
  9. Saurabh Gupta, Ross Girshick, Pablo Arbeláez, and Jitendra Malik. Learning rich features from RGB-D images for object detection and segmentation. In European Conference on Computer Vision, pages 345-360. Springer, 2014. Google Scholar
  10. Yuhang He, Long Chen, Jianda Chen, and Ming Li. A novel way to organize 3D LiDAR point cloud as 2D depth map height map and surface normal map. In Robotics and Biomimetics (ROBIO), 2015 IEEE International Conference on, pages 1383-1388. IEEE, 2015. Google Scholar
  11. Zixuan Huang, Junming Fan, Shuai Yi, Xiaogang Wang, and Hongsheng Li. HMS-Net: Hierarchical Multi-scale Sparsity-invariant Network for Sparse Depth Completion. arXiv preprint, 2018. URL: http://arxiv.org/abs/1808.08685.
  12. Ferran Hurtado, Marc Noy, and Jorge Urrutia. Flipping edges in triangulations. Discrete &Computational Geometry, 22(3):333-346, 1999. Google Scholar
  13. Jason Ku, Ali Harakeh, and Steven L Waslander. In Defense of Classical Image Processing: Fast Depth Completion on the CPU. arXiv preprint, 2018. URL: http://arxiv.org/abs/1802.00036.
  14. Jason Ku, Melissa Mozifian, Jungwook Lee, Ali Harakeh, and Steven Waslander. Joint 3d proposal generation and object detection from view aggregation. arXiv preprint, 2017. URL: http://arxiv.org/abs/1712.02294.
  15. Fangchang Ma, Guilherme Venturelli Cavalheiro, and Sertac Karaman. Self-supervised Sparse-to-Dense: Self-supervised Depth Completion from LiDAR and Monocular Camera. arXiv preprint, 2018. URL: http://arxiv.org/abs/1807.00275.
  16. Nick Schneider, Lukas Schneider, Peter Pinggera, Uwe Franke, Marc Pollefeys, and Christoph Stiller. Semantically guided depth upsampling. In German Conference on Pattern Recognition, pages 37-48. Springer, 2016. Google Scholar
  17. Kiwoo Shin, Youngwook Paul Kwon, and Masayoshi Tomizuka. RoarNet: A Robust 3D Object Detection based on RegiOn Approximation Refinement. arXiv preprint, 2018. URL: http://arxiv.org/abs/1811.03818.
  18. Peter Su and Robert L Scot Drysdale. A comparison of sequential Delaunay triangulation algorithms. Computational Geometry, 7(5-6):361-385, 1997. Google Scholar
  19. Yan Yan, Yuxing Mao, and Bo Li. SECOND: Sparsely Embedded Convolutional Detection. Sensors, 18(10):3337, 2018. Google Scholar
  20. Yin Zhou and Oncel Tuzel. Voxelnet: End-to-end learning for point cloud based 3d object detection. arXiv preprint, 2017. URL: http://arxiv.org/abs/1711.06396.
Questions / Remarks / Feedback
X

Feedback for Dagstuhl Publishing


Thanks for your feedback!

Feedback submitted

Could not send message

Please try again later or send an E-mail
© 2023-2024 Schloss Dagstuhl – LZI GmbH About DROPS Imprint Privacy Contact