Fig. 1. The proposed system framework of environment perception and reconstruction.
Fig. 2. Proposed system framework for environment perception and reconstruction.
Fig. 3. CPU-GPU sequential diagram of proposed 3D reconstruction module.
Fig. 4. Multiple sensors mounted on UGV. (a) LiDAR, (b) CCD camera, (c) IMU, and (d) multiple sensor integration.
Fig. 5. Segmentation result of ground and non-ground points.
Fig. 6. Ground segmentation and object clustering result in LiDAR point clouds.
Fig. 7. Object segmentation speed performances using the proposed CPU-GPU hybrid system and the CPU-based method.
Fig. 8. High-resolution terrain reconstruction results using texture mesh (a) and colored particles (b).
Table 1. Explanation of variables in Fig. 2
Table 2. Explanation of variables in Fig. 3
References
- Y. Matsushita and J. Miura, "On-line road boundary modeling with multiple sensory features, flexible road model, and particle filter," Robotics and Autonomous Systems, vol. 59, no. 5, pp. 274-284, 2011. https://doi.org/10.1016/j.robot.2011.02.009
- J. M. Noguera, R. J. Segura, C. J. Ogayar, and R. Joan-Arinyo, "Navigating large terrains using commodity mobile devices," Computers & Geosciences, vol. 37, no. 9, pp. 1218-1233, 2011. https://doi.org/10.1016/j.cageo.2010.08.007
- W. Song, S. Zou, Y. Tian, S. Fong, and K. Cho, "Classifying 3D objects in LiDAR point clouds with a backpropagation neural network," Human-centric Computing and Information Sciences, vol. 8, article no. 29, 2018.
- S. R. Sukumar, S. Yu, D. L. Page, A. F. Koschan, and M. A. Abidi, "Multi-sensor integration for unmanned terrain modeling," in Proceedings of SPIE 6230: Unmanned Systems Technology VIII. Bellingham, WA: International Society for Optics and Photonics, 2006.
- D. Huber, H. Herman, A. Kelly, P. Rander, and J. Ziglar, "Real-time photo-realistic visualization of 3D environments for enhanced tele-operation of vehicles," in Proceedings of 2009 IEEE 12th International Conference on Computer Vision Workshops (ICCV Workshops), Kyoto, Japan, 2009, pp. 1518-1525.
- J. Elseberg, D. Borrmann, and A. Nuchter, "One billion points in the cloud-an octree for efficient processing of 3D laser scans," ISPRS Journal of Photogrammetry and Remote Sensing, vol. 76, pp. 76-88, 2013. https://doi.org/10.1016/j.isprsjprs.2012.10.004
- D. Gingras, T. Lamarche, J. L. Bedwani, and E. Dupuis, "Rough terrain reconstruction for rover motion planning," in Proceedings of 2010 Canadian Conference on Computer and Robot Vision (CRV), Ottawa, Canada, 2010, pp. 191-198.
- A. Golovinskiy and T. Funkhouser, "Min-cut based segmentation of point clouds," in Proceedings of 2009 IEEE 12th International Conference on Computer Vision Workshops (ICCV Workshops), Kyoto, Japan, 2009, pp. 39-46.
- B. Douillard, J. Underwood, V. Vlaskine, A. Quadros, and S. Singh, "A pipeline for the segmentation and classification of 3D point clouds," in Experimental Robotics. Heidelberg: Springer, 2014, pp. 585-600.
- M. Himmelsbach, F. V. Hundelshausen, and H. J. Wuensche, "Fast segmentation of 3D point clouds for ground vehicles," in Proceedings of 2010 IEEE Intelligent Vehicles Symposium, San Diego, CA, 2010, pp. 560-565.
- J. Wang, R. Lindenbergh, and M. Menenti, "SigVox: a 3D feature matching algorithm for automatic street object recognition in mobile laser scanning point clouds," ISPRS Journal of Photogrammetry and Remote Sensing, vol. 128, pp. 111-129, 2017. https://doi.org/10.1016/j.isprsjprs.2017.03.012
- H. Wang, B. Wang, B. Liu, X. Meng, and G. Yang, "Pedestrian recognition and tracking using 3D LiDAR for autonomous vehicle," Robotics and Autonomous Systems, vol. 88, pp. 71-78, 2017. https://doi.org/10.1016/j.robot.2016.11.014
- A. Broggi, S. Cattani, M. Patander, M. Sabbatelli, and P. Zani, "A full-3D voxel-based dynamic obstacle detection for urban scenario using stereo vision," in Proceedings of 2013 16th International IEEE Conference on Intelligent Transportation Systems-(ITSC), Hague, The Netherlands, 2013, pp. 71-76.
- A. Khatamian and H. R. Arabnia, "Survey on 3D surface reconstruction," Journal of Information Processing Systems, vol. 12, no. 3, pp. 338-357, 2016. https://doi.org/10.3745/JIPS.01.0010
- W. Song, L. Liu, Y. Tian, G. Sun, S. Fong, and K. Cho, "A 3D localisation method in indoor environments for virtual reality applications," Human-centric Computing and Information Sciences, vol. 7, article no. 39, 2017.
- D. Zeng, Y. Dai, F. Li, R. S. Sherratt, and J. Wang, "Adversarial learning for distant supervised relation extraction," Computers, Materials & Continua, vol. 55, no. 1, pp. 121-136, 2018.