95806 Troops

Fengtai, China

95806 Troops

Fengtai, China
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Hao G.,Academy of Equipment Command and Technology | Du X.,Academy of Equipment Command and Technology | Zhao J.,Academy of Equipment Command and Technology | Chen H.,Academy of Equipment Command and Technology | And 2 more authors.
Optical Engineering | Year: 2015

A dense surface reconstruction approach based on the fusion of monocular vision and three-dimensional (3-D) flash light detection and ranging (LIDAR) is proposed. The texture and geometry information can be obtained simultaneously and quickly for stationary or moving targets with the proposed method. Primarily, our 2-D/3-D fusion imaging system including cameras calibration and an intensity-range image registration algorithm is designed. Subsequently, the adaptive block intensity-range Markov random field (MRF) with optimizing weights is presented to improve the sparse range data from 3-D flash LIDAR. Then the energy function is minimized quickly by conjugate gradient algorithm for each neighborhood system instead of the whole MRF. Finally, the experiments with standard depth datasets and real 2-D/3-D images demonstrate the validity and capability of the proposed scheme. © 2015 Society of Photo-Optical Instrumentation Engineers (SPIE).


Hao G.,Academy of Equipment Command and Technology | Du X.,Academy of Equipment Command and Technology | Chen H.,Academy of Equipment Command and Technology | Song J.,95806 Troops | Gao T.,Academy of Equipment Command and Technology
Optical Engineering | Year: 2015

An approach of scale-unambiguous relative pose estimation for space uncooperative targets based on the fusion of low resolution three-dimensional time-of-flight camera and monocular camera is proposed. No a priori knowledge about the targets is assumed. First, a modified range-intensity Markov random field model is presented to quickly reconstruct the range value for each feature point. Second, the scale-ambiguous relative pose estimation algorithm based on extended Kalman filter-unscented Kalman filter-particle filter combination filter is designed in vision simultaneous localization and mapping framework. Third, the overall scale factor estimation approach based on range-intensity fusion image, which takes the feature points' range reconstruction uncertainty as measurement noise, is proposed for the final scale-unambiguous pose estimation. Finally, some simulations demonstrate the validity and capability of the proposed approach. © 2015 Society of Photo-Optical Instrumentation Engineers (SPIE).


Hao G.-T.,Academy of Equipment Command and Technology | Du X.-P.,Academy of Equipment Command and Technology | Song J.-J.,95806 Troops
Yuhang Xuebao/Journal of Astronautics | Year: 2015

Considering the space tumbling non-cooperative target motion uncertainty and without any priori knowledge, a relative pose estimation method based on vision-only simultaneous localization and mapping(SLAM) for space tumbling non-cooperative target is proposed. Firstly, the state transition equation and measurement update equation of Bayes filter model for relative pose estimation is established. Secondly, in order to overcome the detrimental effects caused by large noise covariance of translation process model, the least square estimate is adopted to predict the position parameter and a improved process model is obtained. Finally, a fast and smooth relative pose estimation is realized by using a Bayes filter combining unscented Kalman filter(UKF) and particle filter(PF). Results from both synthetic and real image sequences show that the method is accurate and efficient, and is robust to the changeful target rotation velocity and the errors of feature extraction and data association. ©, 2015, China Spaceflight Society. All right reserved.


Hao G.,Academy of Equipment Command and Technology | Du X.,Academy of Equipment Command and Technology | Song J.,95806 Troops | Song Y.,Academy of Equipment Command and Technology
Guangxue Xuebao/Acta Optica Sinica | Year: 2015

Aim to improve the low resolution and noisy range image from scannerless three-dimensional (3D) LIDAR, a reconstruction approach of sparse range image based on adaptive block grayscale-range Markov random filed (MRF) with optimizing weights is proposed through integrating a monocular camera with high resolution. A grayscale-range MRF multilevel correlogram is established. On this basis a fast interpolation is obtained without the texture copying by using block processing and the resconstruciton speed is improved. The edge penalty factor based on simple linear iterative clustering (SLIC) superpixels segmentation is applied to preserve the image structure details. In order to get a robust performance, both the spatial depth kernel function and grayscale similarity kernel function with adaptive adjustment of standard deviation of kernel funciton for different neighborhood systems are used as guided map. The conjugate gradient algorithm is performed for each neighborhood system to fast optimize the global energy function. The experiments with standard image datasets and real images show that proposed method have better performance than bilinear interpolation,bilateral filter and standard MRF, so that it is effective for realizing the image reconstruction of scannerless 3D LIDAR. ©, 2015, Chinese Optical Society. All right reserved.

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