Lv Dan, Sun Jianfeng, Li Qi, Wang Qi. 3D pose estimation of target based on ladar range image[J]. Infrared and Laser Engineering, 2015, 44(4): 1115-1120.
Citation:
|
Lv Dan, Sun Jianfeng, Li Qi, Wang Qi. 3D pose estimation of target based on ladar range image[J]. Infrared and Laser Engineering, 2015, 44(4): 1115-1120.
|
3D pose estimation of target based on ladar range image
- 1.
National Key Laboratory of Science and Technology on Tunable Laser,Institute of Opto-Electronic,Harbin Institute of Technology,Harbin 150080,China;
- 2.
College of Applied Technology,Qiqihar University,Qiqihar 161006,China
- Received Date: 2014-08-11
- Rev Recd Date:
2014-09-14
- Publish Date:
2015-04-25
-
Abstract
In the target recognition of ladar, the accurate estimation of target pose can effectively simplify the recognition process. The existing PDVA algorithm as a method of target 3D pose estimation is mainly for ground structured targets. This method uses the planar normals of rigid targets as the vectors in the positive direction of the axes in model coordinate system(MCS) to estimate the 3D pose angles of targets, and its effectiveness has been verified by experiments. However, it is time consuming when determining the positive direction vectors of the axes in MCS and affecting the efficiency of the algorithm. In this paper, an improved PDVA algorithm was proposed and a method of clustering center neighborhood discriminant(CCND) was used for accelerating the determination process of positive direction vectors of the axes in MCS. The simulation experiments were performed with four military vehicle models. The results show that the average running time of the improved PDVA algorithm only accounts for about 66% of the PDVA algorithm, and it greatly improves the efficiency of target 3D pose estimation.
-
References
[1]
|
|
[2]
|
Xiao Xingjun, Sun Jianfeng, Liang Xiaoxue, et al. Matching method for laser radar range image with moment invariants[J]. Infrared and Laser Engineering, 2011, 40(7): 1376-1380. (in Chinese) |
[3]
|
Yang Wenxiu, Fu Wenxing, Zhou Zhiwei, et al. Fast three dimensional LIDAR target recognition based on projection dimension reduction[J]. Infrared and Laser Engineering, 2014, 40(S): 1-7. (in Chinese) |
[4]
|
|
[5]
|
|
[6]
|
Neulist J, Armbruster W. Segmentation, classification, and pose estimation of military vehicles in low resolution laser radar images[C]//Proceedings of Laser Radar Technology and Applications X, 2005, 5791: 218-225. |
[7]
|
Grnwall C, Gustafsson F, Millnert M. Ground target recognition using rectangle estimation[J]. IEEE Transactions on Image Processing, 2006, 15: 3401-3409. |
[8]
|
|
[9]
|
|
[10]
|
Tangelder J W H, Veltkamp R C. A survey of content based 3D shape retrieval methods [C]//Proceedings of Shape Modeling Application, 2004: 145-156. |
[11]
|
Adn A, Merchn P, Salamanca S. 3D scene retrieval and recognition with depth gradient images[J]. Pattern Recognition Letters, 2011, 32: 1337-1353. |
[12]
|
|
[13]
|
Taati B, Greenspan M. Local shape descriptor selection for object recognition in range data[J]. Computer Vision and Image Understanding, 2011, 115: 681-694. |
[14]
|
|
[15]
|
Lv Dan, Sun Jianfeng, Li Qi, et al. 3D pose estimation of ground rigid target based on ladar range image[J]. Applied Optics, 2013, 52(33): 8073-8081. |
[16]
|
|
[17]
|
|
[18]
|
Grimson W EL. Object Recognition by Computer[M]. America: MIT Press, 1990. |
[19]
|
|
[20]
|
Paul R P. Robot Manipulators Mathematics Programming and Control [M]. America: MIT Press, 1981. |
[21]
|
|
[22]
|
Dorai C, Jain A K. COSMOS-a representation scheme for 3D free-form objects[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997, 19: 1115-1130. |
[23]
|
Yang Nan, Xiao Jiangchao, Wang Minghai. Estimation of normal and curvature based on point cloud data[J]. Modern Manufacturing Engineering, 2010(7): 35-38. (in Chinese) |
-
-
Proportional views
-