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第43届中国控制会议

作者： Qiang Zhang Chunjie Gu Qi Shi Xinqi Fang Department of Automation School of Electronic Information and Electrical EngineeringShanghai Jiao Tong University East China Branch of State Grid Corporation of China

Indoor positioning is critical for the safe and efficient operation of *** complexity and the high requirements for positioning accuracy of the internal environment in the substation make the choice of indoor positioning technology a significant ***-wideband(UWB) positioning is widely favored due to its high accuracy and strong resistance to interference,but it is susceptible to non-line-of-sight(NLOS) propagation of *** contrast,inertial positioning does not rely on external environmental factors and relies solely on acceleration and angular velocity data obtained by inertial sensors to calculate positioning ***,due to sensor defects,inertial positioning can lead to error accumulation and an inability to maintain long-term positioning *** address these challenges,we propose a UWB/IMU complementary indoor pedestrian positioning method based on pose *** main concept of this method is to use a combination of one primary and one secondary channel in parallel,with the advantages of each being *** UWB positioning results are determined using the weighted least squares(WLS) algorithm,while the inertial positioning results are obtained using the pedestrian dead reckoning(PDR) *** combination of UWB and IMU positioning results through complementary methods leverages the strengths of both technologies to mitigate their individual weaknesses,resulting in a better positioning effect than a single positioning *** effectiveness of the proposed algorithm is verified through experimental results conducted in an actual scene.

关键词： UWB Positioning IMU Positioning Pedestrian Dead Reckoning pose recognition Data Fusion

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Camera pose Estimation Using Collaborative Databases and Single Building Image

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Journal of Geographic Information System 2020年第6期12卷 620-645页

作者： Bernard Semaan Myriam Servières Guillaume Moreau Bilal Chebaro Ambiances Architectures Urbanités Laboratory école Centrale Nantes Nantes France Lebanese University Beirut Lebanon

Cities are in constant change and city managers aim to keep an updated digital model of the city for city governance. There are a lot of images uploaded daily on image sharing platforms (as “Flickr”, “Twitter”, etc.). These images feature a rough localization and no orientation information. Nevertheless, they can help to populate an active collaborative database of street images usable to maintain a city 3D model, but their localization and orientation need to be known. Based on these images, we propose the Data Gathering system for image pose Estimation (DGPE) that helps to find the pose (position and orientation) of the camera used to shoot them with better accuracy than the sole GPS localization that may be embedded in the image header. DGPE uses both visual and semantic information, existing in a single image processed by a fully automatic chain composed of three main layers: Data retrieval and preprocessing layer, Features extraction layer, Decision Making layer. In this article, we present the whole system details and compare its detection results with a state of the art method. Finally, we show the obtained localization, and often orientation results, combining both semantic and visual information processing on 47 images. Our multilayer system succeeds in 26% of our test cases in finding a better localization and orientation of the original photo. This is achieved by using only the image content and associated metadata. The use of semantic information found on social media such as comments, hash tags, etc. has doubled the success rate to 59%. It has reduced the search area and thus made the visual search more accurate.

关键词： pose recognition Building Detection Single Image 2D Map Collaborative Cartography Social Media

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Research on the recognition Algorithm of Body Posture ELM Registration Model Based on Kinect

Research on the Recognition Algorithm of Body Posture ELM Re...

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第40届中国控制会议

作者： Weibo Yu Meifang Zhu Shicheng Wang School of Electrical and Electronic Engineering Changchun University of Technology

Aiming at the problems of low recognition accuracy and low real-time in the existing human motion recognition in the complex background,a registration model recognition algorithm of human posture limit learning machine（ELM） is proposed,which uses Kinect measurement to calculate the joint association *** Kinect,the spatial coordinates of human joints are ***,three-point method is used to calculate the correlation angle of human joints,and the elm registration model corresponding to the correlation angle of the target attitude joints is ***,the movement recognition test of the elm registration model of human body posture is carried out with the joint correlation angle calculated based on Kinect real-time *** experimental results show that this method has high real-time and accuracy,strong robustness and generalization ability,and can identify more pose by defining more joint angles of target pose to establish elm registration model of human pose.

关键词： pose recognition Kinect Extreme learning machine

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