Unmanned aerial vehicle orthogonal laser localization by Gaussian mixture model-based map representation

IF 1.5 Q3 AUTOMATION & CONTROL SYSTEMS
Zeyu Wan, Changjian Jiang, Yu Zhang
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Abstract

Localization is a core problem in mobile robot navigation. Simultaneous localization and mapping (SLAM) costs much for an unmanned aerial vehicle (UAV). This research aims to design an orthogonal laser scan device for localization and to save computation costs. Based on disturbance analysis, residual influences on sensor state are quantitative, and they are related to uncertainty and sensitivity. This research applied the residual selection method to a UAV. The feature point detection utilises multi-scale and Gaussian model fitting techniques to guarantee true positives. The map is represented by Gaussian Mixture Models (GMM) with lower memory costs. The orthogonal laser scan device is composed and placed on a UAV for real-time three-dimensional localization, whose errors are at the centimeter level.

Abstract Image

基于高斯混合模型的无人机正交激光定位地图表示
定位是移动机器人导航中的一个核心问题。同时定位与制图(SLAM)对于无人机(UAV)来说成本很高。本研究旨在设计一种正交激光扫描定位装置,以节省计算成本。基于扰动分析,对传感器状态的残余影响是定量的,它们与不确定性和灵敏度有关。本研究将残差选择方法应用于某型无人机。特征点检测利用多尺度和高斯模型拟合技术来保证真阳性。该映射由具有较低内存开销的高斯混合模型(GMM)表示。研制了正交激光扫描装置,并将其安装在无人机上进行实时三维定位,定位误差在厘米级。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
IET Cybersystems and Robotics
IET Cybersystems and Robotics Computer Science-Information Systems
CiteScore
3.70
自引率
0.00%
发文量
31
审稿时长
34 weeks
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