3D point cloud filtering method for pose measurement application of space non-cooperative targets
Keywords:
Feature of point cloud, Laser position and attitude sensor, Space non-cooperative target pose measurement, Space optical measurement and navigationAbstract
A point cloud feature extraction and filtering method for position and attitude(P&A) sensor of space non-cooperative target was presented, in order to filter the noise in raw point cloud obtained form laser P&A sensor and solve the problem that too many points taken part in the position and attitude computing wasted too much time. Then, using simulation method, the effectiveness of filtering the space rand noise and down-sample of point cloud was verified, and the robustness for target pose and Gauss measurement noise was tested. Finally, with the help of the all physical test platform for non-cooperative targets fly around, approach and capture, using the raw point cloud obtained from laser P&A sensor, the performance of the method in real position and attitude measurement was presented. The test results show that the algorithm achieves 93.1% down sampling of the original point cloud, saves 92.9% of the pose calculation time, which can effectively improve the efficiency of on-orbit data processing and the real-time performance of pose calculation. © 2019, Editorial Board, Journal of Applied Optics. All right reserved.
References
Uolpe R., Sabatini M., Palmering G.B., Pose and shape reconstruction of a noncooperative spacecraft using camera and range measurements, International Journal of Aerospace Engineering, 2017, pp. 1-13, (2017)
Fu W., Wu L., Chen H., Study on local and global point cloud data simplification algorithm, Laser & Infrared, 45, 8, pp. 1004-1008, (2015)
Guo B., Chen X., He Y., K-nearest neighbor denoising algorithm for point cloud data based on RANSAC algorithm, Electronic Measurement Technology, 12, pp. 209-213, (2017)
Liao C., He D., Wang M., Research on a new denoising algorithm for 3D point cloud model, Computer Applications & Software, 34, 4, pp. 281-287, (2017)
Wu L., Shi H., Chen H., Denoising of three-dimensional point data based on classification of feature information, Optics & Precision Engineering, 24, 6, pp. 1465-1473, (2016)
Zhu F., Wang L., Guo S., Et al., Large dynamic range laser ranging system for non-cooperative target, Infrared & Laser Engineering, b12, pp. 8-12, (2014)
Yin F., Chou W., Wu Y., Et al., Sparse unorganized point cloud based relative pose estimation for uncooperative space target, Sensors, 18, 4, (2018)
Guo R., Wang L., Zhu F., Et al., Research on registration algorithm of multiple-view point cloud for non-cooperative spacecraft, Chinese Space Science & Technology, 36, 5, pp. 32-39, (2016)
Opromolla R., Fasano G., Rufino G., Et al., Pose estimation for spacecraft relative navigation using model-based algorithms, IEEE Transactions on Aerospace & Electronic Systems, 53, 1, pp. 431-447, (2017)
Kumar G., Bhatia P.K., A detailed review of feature extraction in image processing systems, Fourth International Conference on Advanced Computing & Communication Technologies, pp. 5-12, (2014)
He Y., Liang B., He J., Et al., Non-cooperative spacecraft pose tracking based on point cloud feature, Acta Astronautica, 139, pp. 213-221, (2017)
Rusu R.B., Bradski G., Thibaux R., Et al., Fast 3D recognition and pose using the viewpoint feature histogram, 2010 Ieee/rsj International Conference on Intelligent Robots and Systems, pp. 2155-2162, (2010)
Prakhya S.M., Liu B., Lin W., B-SHOT: A binary feature descriptor for fast and efficient keypoint matching on 3D point clouds, 2015 Ieee/rsj International Conference on Intelligent Robots and Systems, pp. 1929-1934, (2015)
Xie G., Ye Y., Li J., Et al., Echo characteristics and range error for pulse laser ranging, Chinese Journal of Lasers, 45, 6, (2018)
Wu Z., Du Z., Uncertainty analysis of laser radar measurement system based on error ellipsoid theory, Laser Technology, 41, 1, pp. 29-33, (2017)
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