Zhang Nan, Sun Jianfeng, Jiang Peng, Liu Di, Wang Penghui. Pose estimation algorithms for lidar scene based on point normal vector[J]. Infrared and Laser Engineering, 2020, 49(1): 0105004-0105004(8). doi: 10.3788/IRLA202049.0105004
| Citation:
|
Zhang Nan, Sun Jianfeng, Jiang Peng, Liu Di, Wang Penghui. Pose estimation algorithms for lidar scene based on point normal vector[J]. Infrared and Laser Engineering, 2020, 49(1): 0105004-0105004(8). doi: 10.3788/IRLA202049.0105004
|
Pose estimation algorithms for lidar scene based on point normal vector
-
1. National Key Laboratory of Science and Technology on Tunable Laser, Institute of Opto-Electronic, Harbin Institute of Technology, Harbin 150001, China;
-
2. Science and Technology on Comples System Control and Intelligent Agent Cooperation Laboratory, Beijing 100074, China;
-
3. China Airborne Missile Academy, Louyang 471009, China
- Received Date: 2019-10-05
- Rev Recd Date:
2019-11-15
- Publish Date:
2020-01-28
-
Abstract
Laser imaging radar can obtain point cloud data reflecting the three-dimensional position of the target, directly estimate the three-dimensional attitude angle of the target, and is an important parameter for feature extraction and target registration. To realize the three-dimensional attitude estimation of scenes, an optimized three-dimensional attitude estimation algorithm(OPDVA) based on point normal vector (PDVA) was proposed to solve the problem of large deviation of the positive vector representing the coordinate axis of scene coordinate system(SCS) in real scenes. In this method, remove point normal vectors in other directions in the cluster by RANdom SAmple Consensus (RANSAC) plane model was removed, and the corresponding normal vectors of the optimal fitting plane were the revised SCS coordinate axes. Using rotational transformation and resampling techniques, 3 groups of real scene range image were experimented with rectangular bounding box method, PDVA and OPDVA respectively. The experimental results show that the OPDVA method is superior to the other two methods in pose estimation. The error of pose estimation does not exceed 4°, and it is also suitable for occlusion scenarios.
-
References
|
[1]
|
Kechagias-Stamatis O, Aouf N, Richardson M A. 3D automatic target recognition for future LIDAR missiles[J]. IEEE Transactions on Aerospace and Electronic Systems, 2016, 52(6):2662-2675. |
|
[2]
|
Jung-Un K, Hang-Bong K. A new 3D object pose detection method using LIDAR shape set[J]. Sensors, 2018, 18(3):882. |
|
[3]
|
Biasutti P, Aujol J F, Brédif M, et al. Range-image:Incorporating sensor topology for lidar point cloud processing[J]. Photogrammetric Engineering & Remote Sensing, 2018, 84(6):367-375. |
|
[4]
|
Chen X, Ma J, Zhao H, et al. Survey of automatic target recognition technology for LADAR[C]//International Symposium on Photoelectronic Detection & Imaging:Laser Sensing & Imaging, 2009. |
|
[5]
|
Paquet E, Rioux M, Murching A, et al. Description of shape information for 2-D and 3-D objects[J]. Signal Processing:Image Communication, 2000, 16(1-2):103-122. |
|
[6]
|
Qingji G, Deyu Y, Qijun L, et al. Minimum elastic bounding box algorithm for dimension detection of 3D objects:a case of airline baggage measurement[J]. IET Image Processing, 2018, 12(8):1313-1321. |
|
[7]
|
Grabner A, Roth P M, Lepetit V. 3D pose estimation and 3D model retrieval for objects in the wild[J]. Verlag der Technischen Universitat Graz, 2018, 10(2):3022-3031. |
|
[8]
|
Lv D, Sun J F, Li Q, et al. 3D pose estimation of ground rigid target based on ladar range image[J]. Applied Optics, 2013, 52(33):8073-8081. |
|
[9]
|
Yuan Xuhua. Image multi-target detection and segmentation algorithm based on regional proposed fast intelligent network[J]. Cluster Computing, 2018, 22(2):3385-3393. |
-
-
Proportional views
-
DownLoad: