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Exploring 2D projection and 3D spatial information for aircraft 6D pose

Chinese Journal of Aeronautics 2023年第8期36卷 258-268页

作者： Daoyong FU Songchen HAN BinBin LIANG Xinyang YUAN Wei LI School of Aeronautics and Astronautics Sichuan UniversityChengdu 610065China

The 6D pose estimation is important for the safe take-off and landing of the aircraft using a single rgb image. Due to the large scene and large depth, the exiting pose estimation methods have unstratified performance on the accuracy. To achieve precise 6D pose estimation of the aircraft, an end-to-end method using an rgb image is proposed. In the proposed method, the2D and 3D information of the keypoints of the aircraft is used as the intermediate supervision,and 6D pose information of the aircraft in this intermediate information will be explored. Specifically, an off-the-shelf object detector is utilized to detect the Region of the Interest(Ro I) of the aircraft to eliminate background distractions. The 2D projection and 3D spatial information of the pre-designed keypoints of the aircraft is predicted by the keypoint coordinate estimator(Kp Net).The proposed method is trained in an end-to-end fashion. In addition, to deal with the lack of the related datasets, this paper builds the Aircraft 6D Pose dataset to train and test, which captures the take-off and landing process of three types of aircraft from 11 views. Compared with the latest Wide-Depth-Range method on this dataset, our proposed method improves the average 3D distance of model points metric(ADD) and 5° and 5 m metric by 86.8% and 30.1%, respectively. Furthermore, the proposed method gets 9.30 ms, 61.0% faster than YOLO6D with 23.86 ms.

关键词： 2D and 3D information 6D pose regression aircraft 6D pose estimation End-to-end network rgb image

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VNLSTM-PoseNet: A novel deep ConvNet for real-time 6-DOF camera relocalization in urban streets

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Geo-Spatial Information Science 2021年第3期24卷 422-437页

作者： Ming Li Jiangying Qin Deren Li Ruizhi Chen Xuan Liao Bingxuan Guo State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan UniversityWuhanChina Department of Physics ETH ZurichZurichSwitzerland Department of Land Surveying and Geo-informatics The Hong Kong Polytechnic UniversityHong KongChina

image-based relocalization is a renewed interest in outdoor environments,because it is an important problem with many *** introduces Convolutional Neural Network(CNN)for the first time to realize the real-time camera pose solution based on a single *** order to solve the problem of precision and robustness of PoseNet and its improved algorithms in complex environment,this paper proposes and implements a new visual relocation method based on deep convolutional neural networks(VNLSTM-PoseNet).Firstly,this method directly resizes the input image without cropping to increase the receptive field of the training ***,the image and the corresponding pose labels are put into the improved Long Short-Term Memory based(LSTM-based)PoseNet network for training and the network is optimized by the Nadam ***,the trained network is used for image localization to obtain the camera *** results on outdoor public datasets show our VNLSTM-PoseNet can lead to drastic improvements in relocalization performance compared to existing state-of-theart CNN-based methods.

关键词： Camera relocalization pose regression deep convnet rgb image camera pose

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3D Vehicle Detection Based on LiDAR and Camera Fusion

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Automotive Innovation 2019年第4期2卷 276-283页

作者： Yingfeng Cai Tiantian Zhang Hai Wang Yicheng Li Qingchao Liu Xiaobo Chen Institute of Automative Engineering Jiangsu UniversityZhenjiang 212013China School of Automotive and Traffic Engineering Jiangsu UniversityZhenjiang 212013China

Nowadays,the deep learning for object detection has become more popular and is widely adopted in many *** paper focuses on the research of LiDAR and camera sensor fusion technology for vehicle detection to ensure extremely high detection *** proposed network architecture takes full advantage of the deep information of both the LiDAR point cloud and rgb image in object ***,the LiDAR point cloud and rgb image are fed into the *** a high-resolution feature map is used to generate a reliable 3D object proposal for both the LiDAR point cloud and rgb ***,3D box regression is performed to predict the extent and orientation of vehicles in 3D *** on the challenging KITTI benchmark show that the proposed approach obtains ideal detection results and the detection time of each frame is about 0.12 *** approach could establish a basis for further research in autonomous vehicles.

关键词： Vehicle detection LiDAR point cloud rgb image Fusion

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Extraction of Geometric Features of Wear Particles in Color Ferr ograph images Based on rgb Color Space

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International Journal of Plant Engineering and Management 2003年第3期8卷 171-178页

作者： CHEN Gui-ming, WANG Han-gong, ZHANG Bao-jun, PAN Wei Second Artillery Engineering Institute, Xi′an 710025, P.R.China Second Artillery Engineering Institute Xi'an 710025 P. R. China

This paper analyzes the potential color f ormats of ferrograph images, and presents the algorithms of converting the forma ts to rgb(Red, Green, Blue) color space. Through statistical analysis of wear pa rticles′ geometric features of color ferrograph images in the rgb color space, we give the differences of ferrograph wear particles′ geometric features among rgb color spaces and gray scale space, and calculate their respective distributi ons.

关键词： ferrograph image rgb image gray image geometri c feature.

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