1P1-H02 Development of an attitude measuring method combined with GPS-Gyro/IMU for a small Unmanned Aerial Vehicle(Localization and Mapping (2))

2013 ◽  
Vol 2013 (0) ◽  
pp. _1P1-H02_1-_1P1-H02_4
Author(s):  
Ken WATANABE ◽  
Teppei OTA ◽  
Mitsunori KITAMURA ◽  
Yoshiharu AMANO ◽  
Takumi HASHIZUME
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Measuring Method ◽  
Vehicle Localization ◽  
Localization And Mapping ◽  
Aerial Vehicle

Performance Analysis in Object Diameter Estimation based on Single and Multiple Non-Destructive and Discrete Infrared Sensors

2014 ◽  
Vol 67 (1) ◽  
Author(s):  
Norashikin M. Thamrin ◽  
Norhashim Mohd. Arshad ◽  
Ramli Adnan ◽  
Rosidah Sam ◽  
Noorfazdli Abd. Razak ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Laser Scanning ◽  
Pass Rate ◽  
Small Scale ◽  
Infrared Sensors ◽  
Scanning Method ◽  
Average Percentage ◽  
Multiple Sensors ◽  
Localization And Mapping ◽  
Aerial Vehicle

In Simultaneous Localization and Mapping (SLAM) technique, recognizing and marking the landmarks in the environment is very important. Therefore, in a commercial farm, rows of trees, borderline of rows as well as the trees and other features are mostly used by the researchers in realizing the automation process in this field. In this paper, the detection of the tree based on its diameter is focused. There are few techniques available in determining the size of the tree trunk inclusive of the laser scanning method as well as image-based measurements. However, those techniques require heavy computations and equipments which become constraints in a lightweight unmanned aerial vehicle implementation. Therefore, in this paper, the detection of an object by using a single and multiple infrared sensors on a non-stationary automated vehicle platform is discussed. The experiments were executed on different size of objects in order to investigate the effectiveness of this proposed method. This work is initially tested on the ground, based in the lab environment by using an omni directional vehicle which later will be adapted on a small-scale unmanned aerial vehicle implementation for tree diameter estimation in the agriculture farm.  In the current study, comparing multiple sensors with single sensor orientation showed that the average percentage of the pass rate in the pole recognition for the former is relatively more accurate than the latter with 93.2 percent and 74.2 percent, respectively. 

3D Terrain Reconstruction by Small Unmanned Aerial Vehicle Using SIFT-Based Monocular SLAM

2011 ◽  
Vol 23 (2) ◽  
pp. 292-301 ◽  
Author(s):  
Taro Suzuki ◽  
◽  
Yoshiharu Amano ◽  
Takumi Hashizume ◽  
Shinji Suzuki ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Effective Means ◽  
3D Measurement ◽  
Scale Invariant ◽  
Practical Applications ◽  
Monocular Camera ◽  
3D Terrain ◽  
Localization And Mapping ◽  
Aerial Vehicle ◽  
Slam Algorithm

This paper describes a Simultaneous Localization And Mapping (SLAM) algorithm using a monocular camera for a small Unmanned Aerial Vehicle (UAV). A small UAV has attracted the attention for effective means of the collecting aerial information. However, there are few practical applications due to its small payloads for the 3D measurement. We propose extended Kalman filter SLAM to increase UAV position and attitude data and to construct 3D terrain maps using a small monocular camera. We propose 3D measurement based on Scale-Invariant Feature Transform (SIFT) triangulation features extracted from captured images. Field-experiment results show that our proposal effectively estimates position and attitude of the UAV and construct the 3D terrain map.

scholarly journals A monocular vision–based perception approach for unmanned aerial vehicle close proximity transmission tower inspection

2019 ◽  
Vol 16 (1) ◽  
pp. 172988141882022 ◽  
Author(s):  
Jiang Bian ◽  
Xiaolong Hui ◽  
Xiaoguang Zhao ◽  
Min Tan
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Feature Matching ◽  
Bundle Adjustment ◽  
Monocular Vision ◽  
Transmission Tower ◽  
Data Set ◽  
Vehicle Localization ◽  
Vehicle System ◽  
Close Proximity ◽  
Aerial Vehicle

Employing unmanned aerial vehicles to conduct close proximity inspection of transmission tower is becoming increasingly common. This article aims to solve the two key problems of close proximity navigation—localizing tower and simultaneously estimating the unmanned aerial vehicle positions. To this end, we propose a novel monocular vision–based environmental perception approach and implement it in a hierarchical embedded unmanned aerial vehicle system. The proposed framework comprises tower localization and an improved point–line-based simultaneous localization and mapping framework consisting of feature matching, frame tracking, local mapping, loop closure, and nonlinear optimization. To enhance frame association, the prominent line feature of tower is heuristically extracted and matched followed by the intersections of lines are processed as the point feature. Then, the bundle adjustment optimization leverages the intersections of lines and the point-to-line distance to improve the accuracy of unmanned aerial vehicle localization. For tower localization, a transmission tower data set is created and a concise deep learning-based neural network is designed to perform real-time and accurate tower detection. Then, it is in combination with a keyframe-based semi-dense mapping to locate the tower with a clear line-shaped structure in 3-D space. Additionally, two reasonable paths are planned for the refined inspection. In experiments, the whole unmanned aerial vehicle system developed on Robot Operating System framework is evaluated along the paths both in a synthetic scene and in a real-world inspection environment. The final results show that the accuracy of unmanned aerial vehicle localization is improved, and the tower reconstruction is fast and clear. Based on our approach, the safe and autonomous unmanned aerial vehicle close proximity inspection of transmission tower can be realized.

scholarly journals Simulation and Analysis of Particle Filter Based Slam System

2018 ◽  
Vol 25 (1) ◽  
pp. 137-153
Author(s):  
Piotr Kaniewski ◽  
Paweł Słowak
Keyword(s):  
Particle Filter ◽  
Unmanned Aerial Vehicle ◽  
Simultaneous Localization And Mapping ◽  
Flight Trajectory ◽  
Localization And Mapping ◽  
Aerial Vehicle ◽  
Simulation Results ◽  
Slam Algorithm

AbstractThe paper describes a problem and an algorithm for simultaneous localization and mapping (SLAM) for an unmanned aerial vehicle (UAV). The algorithm developed by the authors estimates the flight trajectory and builds a map of the terrain below the UAV. As a tool for estimating the UAV position and other parameters of flight, a particle filter was applied. The proposed algorithm was tested and analyzed by simulations and the paper presents a simulator developed by the authors and used for SLAM testing purposes. Chosen simulation results, including maps and UAV trajectories constructed by the SLAM algorithm are included in the paper.

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