과제정보
This research was conducted with the support of the 'National R&D Project for Smart Construction Technology (23SMIP-A158708-04)' funded by the Korea Agency for Infrastructure Technology Advancement under the Ministry of Land, Infrastructure and Transport, and managed by the Korea Expressway Corporation
참고문헌
- H. A. Ignatious, H. El-Sayed, and M. Khan, "An overview of sensors in Autonomous Vehicles," Procedia Computer Science, vol. 198, pp. 736-741, 2022, DOI: 10.1016/j.procs.2021.12.315.
- Y. Zhou and O. Tuzel, "VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection," 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, pp. 4490-4499, 2018, DOI: 10.1109/CVPR.2018.00472.
- A. H. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang, and O. Beijbom, "PointPillars: Fast Encoders for Object Detection From Point Clouds," 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA, pp. 12689-12697, 2019, DOI: 10.1109/CVPR.2019.01298.
- S. Vora, A. H. Lang, B. Helou, and O. Beijbom, "PointPainting: Sequential Fusion for 3D Object Detection," 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA, pp. 4603-4611, 2020, DOI: 10.1109/CVPR42600.2020.00466.
- X. Chen, S. Li, B. Mersch, L. Wiesmann, J. Gall, J. Behley, and C. Stachniss, "Moving Object Segmentation in 3D LiDAR Data: A Learning-Based Approach Exploiting Sequential Data," IEEE Robotics and Automation Letters, vol. 6, no. 4, pp. 6529-6536, Oct., 2021, DOI: 10.1109/LRA.2021.3093567.
- P. Wu, S. Chen, and D. N. Metaxas, "MotionNet: Joint Perception and Motion Prediction for Autonomous Driving Based on Bird's Eye View Maps," 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA, pp. 11382-11392, 2020, DOI: 10.1109/CVPR42600.2020.01140.
- B. Mersch, X. Chen, I. Vizzo, L. Nunes, J. Behley, and C. Stachniss, "Receding Moving Object Segmentation in 3D LiDAR Data Using Sparse 4D Convolutions," IEEE Robotics and Automation Letters, vol. 7, no. 3, pp. 7503-7510, Jul., 2022, DOI: 10.1109/LRA.2022.3183245.
- N. Wang, C. Shi, R. Guo, H. Lu, Z.Zheng, and X. Chen, "InsMOS: Instance-Aware Moving Object Segmentation in LiDAR Data," arXiv:2303.03909, Mar., 2023, DOI: 10.48550/arXiv.2303.03909.
- K. Park, G. Im, M. Kim, and J. Park, "Parking Space Detection based on Camera and LIDAR Sensor Fusion," Journal of Korea Robotics Society, vol. 14, no. 3, pp. 170-178, Aug., 2019, DOI: 10.7746/jkros.2019.14.3.170.
- Y. Cho, H. C. Roh, and M. Chung, "Accurate Parked Vehicle Detection using GMM-based 3D Vehicle Model in Complex Urban Environments," The Journal of Korea Robotics Society, vol. 10, no. 1, pp. 33-41, 2015, DOI: 10.7746/jkros.2015.10.1.033.
- D. Song, J.-B. Yi, and S.-J. Yi, "Development of an Efficient 3D Object Recognition Algorithm for Robotic Grasping in Cluttered Environments," The Journal of Korea Robotics Society, vol. 17, no. 3, pp. 255-263, Aug., 2022, DOI: 10.7746/jkros.2022.17.3.255.
- X. Chen, H. Ma, J. Wan, B. Li, and T. Xia, "Multi-view 3D Object Detection Network for Autonomous Driving," 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, pp. 6526-6534, 2017, DOI: 10.1109/CVPR.2017.691.
- J. Ku, M. Mozifian, J. Lee, A. Harakeh, and S. L. Waslander, "Joint 3D Proposal Generation and Object Detection from View Aggregation," 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain, pp. 1-8, 2018, DOI: 10.1109/IROS.2018.8594049.
- R. Q. Charles, H. Su, M. Kaichun, and L. J. Guibas, "PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation," 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, pp. 77-85, 2017, DOI: 10.1109/CVPR.2017.16.
- Y. Yan, Y. Mao, and B. Li, "SECOND: Sparsely embedded convolutional detection," Sensors, vol. 18, no. 10, Oct., 2018, DOI: 10.3390/s18103337.
- W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C. Fu, and A. C. Berg, "SSD: Single Shot MultiBox Detector," Computer Vision-ECCV 2016: 14th European Conference, Amsterdam, pp. 21-37, 2016, DOI: 10.1007/978-3-319-46448-0_2.
- T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollar, "Focal Loss for Dense Object Detection," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 42, no. 2, pp. 318-327, Feb., 2020, DOI: 10.1109/TPAMI.2018.2858826.
- J. Fritsch, T. Kuhnl, and A. Geiger, "A new performance measure and evaluation benchmark for road detection algorithms," 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013), The Hague, Netherlands, pp. 1693-1700, 2013, DOI: 10.1109/ITSC.2013.6728473.
- J. Behley, M. Garbade, A. Milioto, J. Quenzel, S. Behnke, C. Stachniss, and J. Gall, "SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences," 2019 IEEE/CVF International Conference on Computer Vision (ICCV), Seoul, Korea (South), pp. 9296-9306, 2019, DOI: 10.1109/ICCV.2019.00939.
- J. Sun, Y. Dai, X. Zhang, J. Xu, R. Ai, W. Gu, and X. Chen, "Efficient Spatial-Temporal Information Fusion for LiDAR-Based 3D Moving Object Segmentation," 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Kyoto, Japan, pp. 11456-11463, 2022, DOI: 10.1109/IROS47612.2022.9981210.
- X. Chen, B. Mersch, L. Nunes, R. Marcuzzi, I. Vizzo, J. Behley, and C. Stachniss, "Automatic Labeling to Generate Training Data for Online LiDAR-Based Moving Object Segmentation," IEEE Robotics and Automation Letters, vol. 7, no. 3, pp. 6107-6114, Jul., 2022, DOI: 10.1109/LRA.2022.3166544.
- A. Geiger, P. Lenz, and R. Urtasun, "Are we ready for autonomous driving? The KITTI vision benchmark suite," 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA, pp. 3354-3361, 2012, DOI: 10.1109/CVPR.2012.6248074.