[1] Yano H, Kubota T, Miyamoto H, et al. Touchdown of the Hayabusa spacecraft at the Muses Sea on Itokawa[J]. Science, 2006, 312(5778): 1350-1353.

    Yano H, Kubota T, Miyamoto H, et al. Touchdown of the Hayabusa spacecraft at the Muses Sea on Itokawa[J]. Science, 2006, 312(5778): 1350-1353.

[2] Tsuchiyama A, Uesugi M, Matsushima T, et al. Three-dimensional structure of Hayabusa samples: origin and evolution of Itokawa regolith[J]. Science, 2011, 333(6046): 1125-1128.

    Tsuchiyama A, Uesugi M, Matsushima T, et al. Three-dimensional structure of Hayabusa samples: origin and evolution of Itokawa regolith[J]. Science, 2011, 333(6046): 1125-1128.

[3] Tsuda Y, Yoshikawa M, Abe M, et al. System design of the Hayabusa 2-Asteroid sample return mission to 1999 JU3[J]. Acta Astronautica, 2013, 91: 356-362.

    Tsuda Y, Yoshikawa M, Abe M, et al. System design of the Hayabusa 2-Asteroid sample return mission to 1999 JU3[J]. Acta Astronautica, 2013, 91: 356-362.

[4] Tsuda Y, Yoshikawa M, Saiki T, et al. Hayabusa2-Sample return and kinetic impact mission to near-earth asteroid Ryugu[J]. Acta Astronautica, 2019, 156: 387-393.

    Tsuda Y, Yoshikawa M, Saiki T, et al. Hayabusa2-Sample return and kinetic impact mission to near-earth asteroid Ryugu[J]. Acta Astronautica, 2019, 156: 387-393.

[5] Titterton D H. Military Laser Technology and Systems[M]. US: Artech House, 2015.

    Titterton D H. Military Laser Technology and Systems[M]. US: Artech House, 2015.

[6] 卢纯青, 宋玉志, 武延鹏, 等. 基于C-TOF计算成像的三维信息获取与误差分析[J]. 红外与激光工程, 2018, 47(10): 1041004.

    卢纯青, 宋玉志, 武延鹏, 等. 基于C-TOF计算成像的三维信息获取与误差分析[J]. 红外与激光工程, 2018, 47(10): 1041004.

    Lu Chunqing, Song Yuzhi, Wu Yanpeng, et al. 3D information acquisition and error analysis based on C-TOF computational imaging[J]. Infrared and Laser Engineering, 2018, 47(10): 1041004. (in Chinese)

    Lu Chunqing, Song Yuzhi, Wu Yanpeng, et al. 3D information acquisition and error analysis based on C-TOF computational imaging[J]. Infrared and Laser Engineering, 2018, 47(10): 1041004. (in Chinese)

[7] 卢纯青, 宋玉志, 武延鹏, 等. 基于相关法飞行时间三维感知的误差机理研究[J]. 红外与激光工程, 2019, 48(11): 1113002.

    卢纯青, 宋玉志, 武延鹏, 等. 基于相关法飞行时间三维感知的误差机理研究[J]. 红外与激光工程, 2019, 48(11): 1113002.

    Lu Chunqing, Song Yuzhi, Wu Yanpeng, et al. Theoretical investigation on correlating time-of-flight 3D sensation error[J]. Infrared and Laser Engineering, 2019, 48(11): 1113002. (in Chinese)

    Lu Chunqing, Song Yuzhi, Wu Yanpeng, et al. Theoretical investigation on correlating time-of-flight 3D sensation error[J]. Infrared and Laser Engineering, 2019, 48(11): 1113002. (in Chinese)

[8] Rusu R B, Blodow N, Beetz M. Fast point feature histograms (FPFH) for 3D registration[C]//2009 IEEE International Conference on Robotics and Automation. IEEE, 2009: 3212-3217.

    Rusu R B, Blodow N, Beetz M. Fast point feature histograms (FPFH) for 3D registration[C]//2009 IEEE International Conference on Robotics and Automation. IEEE, 2009: 3212-3217.

[9] Zhang Z. Iterative point matching for registration of free-form curves and surfaces[J]. International Journal of Computer Vision, 1994, 13(2): 119-152.

    Zhang Z. Iterative point matching for registration of free-form curves and surfaces[J]. International Journal of Computer Vision, 1994, 13(2): 119-152.

[10] Rusinkiewicz S, Levoy M. Efficient variants of the ICP algorithm[C]//3-D Digital Imaging and Modeling, 2001. Proceedings. Third International Conference on. IEEE, 2001: 145-152.

    Rusinkiewicz S, Levoy M. Efficient variants of the ICP algorithm[C]//3-D Digital Imaging and Modeling, 2001. Proceedings. Third International Conference on. IEEE, 2001: 145-152.

[11] Biber P, Strasser W. The normal distributions transform: A new approach to laser scan matching[C]//Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems(IROS 2003)(Cat. No. 03CH37453). IEEE, 2003, 3: 2743-2748.

    Biber P, Strasser W. The normal distributions transform: A new approach to laser scan matching[C]//Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems(IROS 2003)(Cat. No. 03CH37453). IEEE, 2003, 3: 2743-2748.

[12] 卢纯青, 刘兴潭, 武延鹏, 等. 飞行时间成像系统的数字仿真技术[J]. 空间控制技术与应用, 2019, 45(2): 56-66.

    卢纯青, 刘兴潭, 武延鹏, 等. 飞行时间成像系统的数字仿真技术[J]. 空间控制技术与应用, 2019, 45(2): 56-66.

    Lu Chunqing, Liu Xingtan, Wu Yanpeng, et al. Computer simulation technology of time-of-flight imaging system[J]. Aerospace Control and Application, 2019, 45(2): 56-66. (in Chinese)

    Lu Chunqing, Liu Xingtan, Wu Yanpeng, et al. Computer simulation technology of time-of-flight imaging system[J]. Aerospace Control and Application, 2019, 45(2): 56-66. (in Chinese)

[13] Yue X, Wu B, Seshia S A, et al. A lidar point cloud generator: from a virtual world to autonomous driving[C]//Proceedings of the 2018 ACM on International Conference on Multimedia Retrieval. ACM, 2018: 458-464.

    Yue X, Wu B, Seshia S A, et al. A lidar point cloud generator: from a virtual world to autonomous driving[C]//Proceedings of the 2018 ACM on International Conference on Multimedia Retrieval. ACM, 2018: 458-464.

[14] Wu B, Wan A, Yue X, et al. Squeezeseg: Convolutional neural nets with recurrent crf for real-time road-object segmentation from 3d lidar point cloud[C]//2018 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2018: 1887-1893.

    Wu B, Wan A, Yue X, et al. Squeezeseg: Convolutional neural nets with recurrent crf for real-time road-object segmentation from 3d lidar point cloud[C]//2018 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2018: 1887-1893.

[15] Wu B, Zhou X, Zhao S, et al. Squeezesegv2: Improved model structure and unsupervised domain adaptation for road-object segmentation from a lidar point cloud[C]//2019 International Conference on Robotics and Automation (ICRA). IEEE, 2019: 4376-4382.

    Wu B, Zhou X, Zhao S, et al. Squeezesegv2: Improved model structure and unsupervised domain adaptation for road-object segmentation from a lidar point cloud[C]//2019 International Conference on Robotics and Automation (ICRA). IEEE, 2019: 4376-4382.

[16] Srivastava N, Hinton G, Krizhevsky A, et al. Dropout: a simple way to prevent neural networks from overfitting[J]. Journal of Machine Learning Research, 2014, 15(1): 1929-1958.

    Srivastava N, Hinton G, Krizhevsky A, et al. Dropout: a simple way to prevent neural networks from overfitting[J]. Journal of Machine Learning Research, 2014, 15(1): 1929-1958.