Affiliation:
1. National Key Lab of Autonomous Intelligent Unmanned Systems, School of Automation, Beijing Institute of Technology, No. 5, Zhongguancun South Street, Beijing 100081, P. R. China
2. Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition and MOE Key Lab of Nondestructive Testing Technology, Nanchang Hangkong University, Nanchang 330063, P. R. China
Abstract
Accurate and robust state estimation is critical for the heterogeneous agent systems, particularly when considering the challenges posed by Unmanned Aerial Vehicles (UAVs) operating in perceptually-degraded environments where access to Global Navigation Satellite System (GNSS) signals is lost. We can, however, actively increase the amount of optimal localization available to UAV by augmenting them with a small number of more expensive, but less resource-constrained, heterogeneous agents. In this paper, we propose a novel detection, localization, and tracking framework for UAV based on LiDAR. First, we present an innovative approach that integrates range image projection and Depth Cluster of LiDAR point clouds with UAV technology. Subsequently, we devise a multidimensional feature probability detection and tracking evaluation function, enabling the detection, estimation, and active tracking of UAV movement. Finally, we conduct comprehensive experiments using heterogeneous agent systems to assess the effectiveness and robustness of the developed framework. The experiments reveal a minimum 20% reduction in running time and an average localization accuracy error of 1.98[Formula: see text]cm.
Funder
National Natural Science Foundation of China
Foundation of Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition
the Jiangxi Natural Science Foundation
the Foundation of Science and Technology of Jiangxi Provincial Education Department
Doctor Science Foundation of Long Dong University
Publisher
World Scientific Pub Co Pte Ltd
Cited by
7 articles.
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