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A new pose estimation method for non-cooperative spacecraft based on point cloud

International Journal of Intelligent Computing and Cybernetics 2019年第1期12卷 23-41页

作者： Zhiming Chen Lei Li Yunhua Wu Bing Hua Kang Niu College of Astronautics Nanjing University of Aeronautics and AstronauticsNanjingChina Shanghai Institute of Mechanical and Electrical Engineering ShanghaiChina

Purpose–On-orbitservice technologyis one of the key technologies of space manipulation activities such as spacecraft life extension,fault spacecraft capture,on-orbit debris removal and so *** is known that the failure satellites,space debris and enemy spacecrafts in space are almost all non-cooperative *** accurate pose estimation is critical to spatial operations,but also a recognized technical difficulty because of the undefined prior information of non-cooperative *** the rapid development of laser radar,the application of laser scanning equipment is increasing in the measurement of non-cooperative *** is necessary to research a new pose estimation method for non-cooperative targets based on 3D point *** paper aims to discuss these ***/methodology/approach–In this paper,a method based on the inherent characteristics of a spacecraft is proposed for estimating the pose(position and attitude)of the spatial non-cooperative ***,we need to preprocess the obtained point cloud to reduce noise and improve the quality of ***,according to the features of the satellite,a recognition system used for non-cooperative measurement is *** components which are common in the configuration of satellite are chosen as the recognized ***,based on the identified object,the ICP algorithm is used to calculate the pose between two frames of point cloud in different times to finish pose ***–The new method enhances the matching speed and improves the accuracy of pose estimation compared with traditional methods by reducing the number of matching *** recognition of components on non-cooperative spacecraft directly contributes to the space docking,on-orbit capture and relative *** limitations/implications–Limited to the measurement distance of the laser radar,this paper considers the pose estimation for non-cooperative spacecraft in the close *** implications–

关键词： Point cloud Pose estimation ICP algorithm Non-cooperative spacecraft ransac algorithm

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Depth segmentation in real-world scenes based on U-V disparity analysis

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JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION 2020年 73卷 102920-102920页

作者： Li, Xiaohan Chen, Lu Li, Shuang Zhou, Xiang Xi An Jiao Tong Univ Sch Mech Engn 28 Xianning West Rd Xian 710049 Peoples R China Xi An Jiao Tong Univ Sch Food Equipment Engn & Sci 28 Xianning West Rd Xian 710049 Peoples R China

Depth segmentation has the challenge of separating the objects from their supporting surfaces in a noisy environment. To address the issue, a novel segmentation scheme based on disparity analysis is proposed. First, we transform a depth scene into the corresponding U-V disparity map. Then, we conduct a region-based detection method to divide the object region into several targets in the processed U-disparity map. Thirdly, the horizontal plane regions may be mapped as slant lines in the V-disparity map, the Random Sample Consensus (ransac) algorithm is improved to fit such multiple lines. Moreover, noise regions are reduced by image processing strategies during the above processes. We respectively evaluate our approach on both real-world scenes and public data sets to verify the flexibility and generalization. Sufficient experimental results indicate that the algorithm can efficiently segment and label a full-view scene into a group of valid regions as well as removing surrounding noise regions.

关键词： Depth scene segmentation U-V disparity map Projection characteristics analysis Object detection ransac algorithm

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Research on Improved Image Registration algorithm Based on PROSAC algorithm 11

Research on Improved Image Registration Algorithm Based on P...

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11th International Conference on Digital Image Processing (ICDIP)

作者： Li, Xiangyu Lei, Tianjie Zhao, Chun Huang, Jintao Yuan, Man Wang, Jiabao China Inst Water Resources & Hydropower Res IWHR Beijing 100038 Peoples R China North China Univ Water Resources & Elect Power NC Zhengzhou 450000 Peoples R China

ISBN: (数字)9781510630765

ISBN: (纸本)9781510630765

In order to solve the problems of poor stability and multiple mismatching points in image registration, most scholars have used Random Sample Consensus (ransac) algorithm to optimize the matching algorithm. However, because of the randomness of the ransac algorithm itself, the matching algorithm has poor stability, low registration efficiency and poor robustness. To solve this problem, an improved SIFT (Scale-invariant feature transform) image registration optimization algorithm based on PROSAC (Progressive Sampling Consensus) was proposed. The experimental results showed that the proposed image registration optimization algorithm could effectively solve the problems of error matching and low efficiency in the process of image matching. Using the same image to test, the average correct registration rate of the traditional ransac improved SIFT algorithm was 82%, and the average running time was 36 seconds. The average correct registration rate of the SIFT image registration algorithm based on PROSAC improved SIFT image registration algorithm was 86.67%, the average running time was 26.51 seconds, and the running efficiency was increased by 36%. Therefore, the improved SIFT image registration algorithm based on PROSAC has higher robustness, can meet the needs of fast image mosaic, and has broad application prospects.

关键词： Image stitching SIFT algorithm ransac algorithm PROSAC algorithm

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On-road Multiple Obstacles Detection Using Color Images and LiDAR Point Clouds 7

On-road Multiple Obstacles Detection Using Color Images and ...

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7th International Conference on Optical and Photonic Engineering (icOPEN)

作者： Xu Zhe Rao Jinjun Hu Wei Chen Jinbo Wang Tao Liu Mei Lei Jingtao Shanghai Univ Dept Precis Mech Engn Shanghai Peoples R China

ISBN: (数字)9781510631601

ISBN: (纸本)9781510631601

Obstacles detection is one of the most important parts for ADAS (Advanced Driver Assistance Systems). Camera provides excellent recognition but with limits to range information;nevertheless, the LiDAR allows for better range information but with limits to the object identification. This paper deals with the problem of efficiently and accurately detecting vehicles on-load by fusing color images and LiDAR point clouds. Firstly, a neural network is used to detect road and vehicles. This neural network has high accuracy and speed on detection for the encoder in it is shared by different tasks. In the second step, the point clouds are processed to remove some invalid points and positions that potential represent targets generate by clustering point clouds. Positions are projected to images plane to get the ROI (Region of Interest), then the ROI will be matched with detection results of image to check if any targets are missed. In the paper, we adopt the ransac (Random Sample Consensus) algorithm to remove ground points. A parameter adaptive DBSCAN (Density- Based Spatial Clustering of Applications with Noise) algorithm is proposed to cluster points, where parameters can change adaptively according to the characteristics of different density point clouds. Through neural network, we recognize the types of obstacles. Experiment is performed on KITTI dataset, using left color images and Velodyne64 point clouds to verify our method. The result shows satisfactory accuracy in detection work.

关键词： On-road targets detection color images LiDAR point clouds ransac algorithm parameter adaptive DBSCAN algorithm neural network

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Comparisons of Planar Detection for Service Robot with ransac and Region Growing algorithm 36

Comparisons of Planar Detection for Service Robot with RANSA...

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第36届中国控制会议

作者： Zhijun Zhang Yongqian Huang Wenkang Zhang Jiawei Luo IEEE The School of Automation Science and Engineering South China University of Technology Pingxiang University

ISBN: (纸本)9781538629185

Considering the life independence of people with disabilities or a limited range of movement,we intend to develop an intelligent mobile service robot equipped with robot manipulator and computer *** order to implement an automatic moving,grasping and delivering task,the abilities of service robot to deal with the images they see and collect useful information are really *** this paper,two different algorithms(i.e.,the ransac algorithm and the region growing algorithm)of the planar detection for the service robot are introduced with detailed principles and implementation *** the foundation of our mobile service robot's development,three specific experiments are designed to compare the algorithms on accuracy,robustness and computational ***,after the comparison between two algorithms,the suitability of two algorithms for the service robot using in the environment detection will be presented and analyzed.

关键词： Service Robot Computer Vision Planar Detection 3D Point Cloud ransac algorithm Region Growing algorithm

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A Robust Direction of Arrival Estimation Method for Uniform Circular Array

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SENSORS 2019年第20期19卷 4427-4427页

作者： Xu, Zhengqin Wu, Shiqian Yu, Zhuliang Guang, Xingyu Wuhan Univ Sci & Technol Sch Machinery & Automat Wuhan 430081 Hubei Peoples R China Wuhan Univ Sci & Technol Inst Robot & Intelligent Syst Sch Informat Sci & Engn Wuhan 430081 Hubei Peoples R China South China Univ Technol Sch Automat Sci & Engn Guangzhou 510640 Guangdong Peoples R China

Estimating the Direction of Arrival (DOA) is a basic and crucial problem in array signal processing. The existing DOA methods fail to obtain reliable and accurate results when noise and reverberation occur in real applications. In this paper, an accurate and robust estimation method for estimating the DOA of sources signal is proposed. Incorporating the Estimating Signal Parameters via Rotational Invariance Techniques (ESPRIT) algorithm with the RANdom SAmple Consensus (ransac) algorithm gives rise to the RAN-ESPRIT method, which removes outliers automatically in noise-corrupted environments. In this work, a uniform circular array (UCA) is converted into a virtual uniform linear array (ULA) to begin with. Then, the covariance matrix of the received signals of the virtual linear array is reconstructed, and the ESPRIT algorithm is deployed to estimate initial DOA of the source signal. Finally, the modified ransac method with automatically selected thresholds is used to fit the source signal to obtain accurate DOA. The proposed method can remove the unreliable DOA feature data and leads to more accuracy of DOA estimation of source signals in reverberation environments. Experimental results demonstrate that the proposed method is more robust and efficient compared to the traditional methods (i.e., ESPRIT, TLS-ESPRIT).

关键词： DOA estimation ransac algorithm uniform circular array ESPRIT algorithm

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Research on an Improved algorithm for Eliminating Gross Error in UAV Image Mosaic 26

Research on an Improved Algorithm for Eliminating Gross Erro...

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26th International Conference on Geoinformatics (Geoinformatics)

作者： Xi, Wenfei Li, Dongsheng Li, Jingshan Ling, Zhen Yunnan Normal Univ Coll Tourism & Geog Sci Kunming 650050 Yunnan Peoples R China Ctr Geospatial Informat Engn & Technol Yunnan Pro Kunming 650500 Yunnan Peoples R China Key Lab Resources & Environm Remote Sensing Univ Kunming 650500 Yunnan Peoples R China Kunming Met Coll Kunming 650033 Yunnan Peoples R China Yunnan Huineng Energy Trading Co Ltd Kunming 650224 Yunnan Peoples R China Kunming Univ Urban & Rural Construct & Engn Management Fac Kunming 650214 Yunnan Peoples R China

ISBN: (纸本)9781538676196

High resolution images can be obtained by using UAV technology. In order to obtain high precision homographic matrix and to improve image matching efficiency, this paper improves ransac algorithm, eliminates match gross error through by adding image gray level information. Then the root mean square error is used to evaluate quality of image matching. In order to verify the reliability of the algorithm, a series of buildings images were taken to test the algorithm. The results show that rate of gross error match points elimination was increased by 7.45%, and the new algorithm had a small root mean square error, which increased the accuracy significantly.

关键词： UAV Homographic matrix Image matching ransac algorithm Gray information Root mean square error

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Lane Detection using Spline Model for Freeway Aerial Videos 10

Lane Detection using Spline Model for Freeway Aerial Videos

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10th International Conference on Digital Image Processing (ICDIP)

作者： Li, Yongbin Lu, Xiaobo Tang, Tao Southeast Univ Sch Automat Nanjing 210096 Jiangsu Peoples R China Southeast Univ Minist Educ Key Lab Measurement & Control CSE Nanjing 210096 Jiangsu Peoples R China

ISBN: (数字)9781510622005

ISBN: (纸本)9781510622005

This paper proposes a lane detection algorithm based on spline model in aerial video of the freeway, which consists of three sections: image segmentation, clustering of lane feature points and lane model parameter estimation. Firstly, the segmentation method is based on the characteristics of lane, such as color, width and shape. In the aspect of clustering of lane feature points, spectral clustering algorithm is used to accomplish the clustering of effective feature points, and the similarity matrix is constructed according to the line spacing. In terms of lane model selection and parameter estimation, we fulfill them with the following three procedures: 1) cubic B-spline curve is used in this paper to express the lane more accurately and to indicate the distance farther. 2) we evaluated the model parameters by taking advantage of the improved ransac algorithm. 3) we chose the Kalman filter to correct and predict lane parameters. The results of experiment demonstrate that the proposed method can detect the model parameters of every lane from the video of aerial photography freeway with high stability and high detection accuracy.

关键词： lane detection Hough Transform clustering spline model ransac algorithm

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Water Quality Anomaly Monitoring Based on Kalman Filter and Convolution Neural Network

Water Quality Anomaly Monitoring Based on Kalman Filter and ...

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作者： Shu-chun CHENG Kao-peng ZHAO Ying WANG Shu-hong CHENG No.1 Mine of No.3 Plant of Daqing Oilfield School of Information Science and Engineering Yanshan University

Aiming at the problem of integrated environmental monitoring of water quality pollution, a method of water quality anomaly monitoring using Kalman filter and Convolution Neural Network(CNN) is proposed. In this method, the fish is segmented by Mask R-CNN image segmentation method, the positive and negative sample data sets of the backbone and background images of the fish are made, and the model is obtained by using convolution neural network training data set. In the process of tracking, the ransac algorithm is used to screen the SIFT feature matching and Kalman filter is used to track the fish and draw the moving track in real time. The motion trajectory is saved every 3 seconds, a total of 150000 samples of normal and abnormal water quality are obtained. The experimental results show that the recognition rate of water quality anomaly based on Kalman filter and Convolution Neural Network is 98.5%, this method is superior to traditional water quality identification methods.

关键词： Calman filter Convolution Neural Network ransac algorithm Water quality anomaly monitoring

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Performance Assessment of Robust & Efficient Video Stabilization algorithms based on L1-L2 Optimization and s-R-t Transform 4

Performance Assessment of Robust & Efficient Video Stabiliza...

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4th International Conference for Convergence in Technology (I2CT)

作者： Kulkarni, S. M. Bormane, D. S. Jha, Chankya Kumar JNTU Kakinada Kakinada India PVPIT E&Tc Pune Maharashtra India AISSMS Coll Engn Pune Maharashtra India Savitribai Phule Pune Univ Pune Maharashtra India

ISBN: (纸本)9781538652329

Video stabilization is the method of removing unwanted movement from a video stream. In this paper, we have proposed three algorithms for stabilization of jittery videos. 1. Video stabilization based on L1 norm 2. Video stabilization based on s-R-t transform 3. Video stabilization based on L1&L2 norm The first algorithm is based on L1 norm. L-1 norm is related with Least Absolute Deviation (LAD).It is minimising sum of absolute difference between consecutive video frames. In the second algorithm, hybrid technique which is the combination of ransac (Random Sample consensus algorithm) and s-R-t (scale-rotation-translation) transform is proposed to stabilize jittery videos. ransac algorithm is used to find effective inlier correspondences and afterward it derives the affine transformation to map the inliers in consecutive video frames. This transformation is capable to improve the image plane. This transform makes smoothening of video frames and also removes jitter in video. To obtain the optimal camera path composed of distinct constant, linear and parabolic segments, we have minimised the first, second, and third derivatives of the resulting camera path. The third algorithm based on L1-L2-norm. L2 optimization achieves the best estimation in least square sense. In order to keep the boundary information of original videos as much as possible optimal smooth camera path should be close to the original path. lambda is a weight to adjust the smoothness of path. It can be treated as a factor which controls the degree of stabilization. Comparing the stabilized and shaky video it is confirmed that the processed videos highly satisfy the human perception. Results indicate a remarkable elimination of high jitter from shaky videos.

关键词： Video Stabilization L1 optimization L1-L2 optimization ransac algorithm Inlier and outliers correspondences Affine Transform s-R-t Transform

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