Ming Li
Wuhan University
32 Papers
154 Citations
Ming Li is an academic researcher from Wuhan University. The author has contributed to research in topics: Computer science & Robustness (computer science). The author has an hindex of 10, co-authored 30 publications. Previous affiliations of Ming Li include ETH Zurich & Heidelberg University.
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Papers
Identifying the city center using human travel flows generated from location-based social networking data:
TL;DR: Experiments show that city centers with precise boundaries can be identified by using the proposed approach with location-based social network data and the results show that the three methods for clustering have different advantages and disadvantages during the process of city center identification, and thus seem to be suitable for cities with different urban structures.
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A Localization Database Establishment Method Based on Crowdsourcing Inertial Sensor Data and Quality Assessment Criteria
TL;DR: Tests with multiple people and multiple smartphones in a public office building and a shopping mall illustrate that the proposed method can provide a WiFi fingerprinting database that has similar accuracy to that generated by a supervised map-aided database-generation method.
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A Survey on Visual Navigation and Positioning for Autonomous UUVs
TL;DR: The two types of SOTA methods are compared experimentally and quantitatively using a public underwater dataset and their potentials and shortcomings are analyzed, providing a panoramic theoretical reference and technical scheme comparison for UUV visual navigation and positioning research in the highly dynamic and three-dimensional ocean environments.
A study on automatic uav image mosaic method for paroxysmal disaster
Ming Li,Deren Li,Dengke Fan +2 more
TL;DR: A novel and fast strategy is proposed for registering and mosaicing UAV data and the best seamline searching strategy based on dynamic schedule is applied to solve the dodging problem arose by aeroplane's side-looking.
A Precise Indoor Visual Positioning Approach Using a Built Image Feature Database and Single User Image from Smartphone Cameras
TL;DR: Results of the experiments indicate that the proposed approach can be used for indoor positioning, with an accuracy of approximately 10 cm, and experiments show that the method is more robust and efficient than the baseline method in a real scene.
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