- 标题
- 摘要
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Framework for automated registration of UAV and UGV point clouds using local features in images
摘要： Automatically registering 3D point clouds generated by unmanned aerial and ground vehicles (UAVs and UGVs) is challenging, as data is acquired at different locations with different sensors, consequently resulting in different spatial scales and occlusions. To address these problems, this study proposes a framework for the automated registration of UAV and UGV point clouds using 2D local feature points in the images taken from UAVs and UGVs. This study first conducted field experiments by varying the angles of the UAV camera to identify the optimal angle with which to detect sufficient points matching with the images taken by the UGV. As a result, this study identified that a combination of UAV images taken at 30° and 90° is appropriate for generating a sufficient number of matching points and attaining a reasonable level of precision. The UAV and UGV point clouds were initially scaled and registered with a transformation matrix computed from the 3D points corresponding to the 2D feature matching points. The initially aligned point clouds were subsequently adjusted by the Iterative Closest Point (ICP) algorithm, resulting in the root mean square error (RMSE) of 0.112 m. This promising result indicates that full automation of spatial data collection and registration from a scattered environment (e.g., construction or disaster sites) by UAVs and UGVs is feasible without human intervention.
关键词： UGV,UAV,Registration,Point cloud,Drone,Mobile robot,Automation
更新于2025-09-10 09:29:36
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[IEEE 2018 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS) - Bangkok, Thailand (2018.10.21-2018.10.24)] 2018 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS) - Research on Laser Navigation Mapping and Path Planning of Tracked Mobile Robot Based on Hector SLAM
摘要： Artificial intelligence and automation technology that can satisfy the ever-increasing demand for technology in industrial, commercial, medical, military and civil fields has introduced new opportunities for robotics research in past decades. In particular, the mobile robot laser navigation has always been the focus and problems in fields covering a wide spectrum of disciplines. The object of this study was to explore the mapping of mobile robot based on Hector-SLAM algorithm and the simulation experiment of the global path planning in static environments. A tracked mobile robot is designed for navigation test platform, which is mainly composed of two dimensional laser radar called Neato XV-11 laser, mobile robot chassis, PC terminal and an Android phone. The motion control commands of robot platform are published by smart phone and executed via Python code in Raspberry Pi board. After algorithm procedure for Hector SLAM are represented in depth, the map building in this way is completed on Robot Operating System (ROS) where RVIZ is run to carry out cartographic visualization. On the other hand, a new ant colony algorithm(ACO), which introduces pheromone orientation, is proposed for the issue about robot shortest path planning in static environment. The results of mapping obtained on ROS show a satisfactory level of indoor environment, which reveals the feasibility of our approach in laser navigation. Furthermore, it is proved from the simulation experiments of modified ACO algorithm that the new algorithm not only can obtain the same optimal path, but also has faster convergence speed and smaller error peak in complex static environments.
关键词： mapping,ant colony algorithm,laser navigation,Hector SLAM,path planning,mobile robot
更新于2025-09-04 15:30:14
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[IEEE NAECON 2018 - IEEE National Aerospace and Electronics Conference - Dayton, OH, USA (2018.7.23-2018.7.26)] NAECON 2018 - IEEE National Aerospace and Electronics Conference - Real-time 3D scene reconstruction and localization with surface optimization
摘要： A real-time 3D scene reconstruction and localization system with surface optimization is proposed. The dense 3D point cloud model is created by utilizing rotation and orientation invariant feature matching along with loop-closure detection algorithm on RGB-D images in a mobile robot. The high resolution and smooth mesh model is implemented on a GPU based computer through wireless communication.
关键词： localization,RGB-D images,surface optimization,mobile robot,GPU,3D scene reconstruction
更新于2025-09-04 15:30:14