[1] [1] Lai K, Bo L F, Ren X F, et al. A large-scale hierarchical multi-view RGB-D object dataset[C]//Proceedings of 2011 IEEE International Conference on Robotics and Automation, 2011: 1817–1824.

[2] [2] Paulk D, Metsis V, McMurrough C, et al. A supervised learning approach for fast object recognition from RGB-D data[C]//Proceedings of the 7th International Conference on PErvasive Technologies Related to Assistive Environments, 2014: 5.

[3] [3] Bo L F, Ren X F, Fox D. Depth kernel descriptors for object recognition[C]//Proceedings of 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2011: 821–826.

[4] [4] Blum M, Springenberg J T, Wülfing J, et al. A learned feature descriptor for object recognition in RGB-D data[C]//Proceedings of 2012 IEEE International Conference on Robotics and Automation, 2012: 1298–1303.

[7] [7] Socher R, Huval B, Bhat B, et al. Convolutional-recursive deep learning for 3D object classification[C]//Proceedings of the 25th International Conference on Neural Information Processing Systems, 2012: 665–673.

[9] [9] Eitel A, Springenberg J T, Spinello L, et al. Multimodal deep learning for robust RGB-D object recognition[C]//Proceedings of 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2015: 681–687.

[10] [10] Aakerberg A, Nasrollahi K, Rasmussen C B, et al. Depth value pre-processing for accurate transfer learning based RGB-D object recognition[C]//Proceedings of the International Joint Conference on Computational Intelligence, 2017: 121–128.

[11] [11] Liu H P, Li F X, Xu X Y, et al. Multi-modal local receptive field extreme learning machine for object recognition[J]. Neurocomputing, 2018, 277: 4–11.

[12] [12] Asif U, Bennamoun M, Sohel F A. RGB-D object recognition and grasp detection using hierarchical cascaded forests[J]. IEEE Trans Robot, 2017, 33(3): 547–564.

[14] [14] Cheng Y H, Cai R, Zhao X, et al. Convolutional fisher kernels for RGB-D object recognition[C]//Proceedings of 2015 International Conference on 3D Vision, 2015: 135–143.

[15] [15] Szegedy C, Liu W, Jia Y Q, et al. Going deeper with convolutions[C]//Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition, 2015: 1–9.

[16] [16] Li X, Zhao Z F, Liu L, et al. An optimization model of multi-intersection signal control for trunk road under collaborative information[J]. J Control Sci Eng, 2017, 2017: 2846987.

[18] [18] Ren X F, Bo L F, Fox D. RGB-(D) scene labeling: features and algorithms[C]//Proceedings of 2012 IEEE Conference on Computer Vision and Pattern Recognition, 2012: 2759–2766.

[19] [19] Schwarz M, Schulz H, Behnke S. RGB-D object recognition and pose estimation based on pre-trained convolutional neural network features[C]//Proceedings of 2015 IEEE International Conference on Robotics and Automation (ICRA), 2015: 1329–1335.