Simultaneous Localization and Mapping Algorithm based on 3D Laser for Unmanned Aerial Vehicle

2020 ◽  
pp. 1003-1020
Author(s):  
Fethi Demim ◽  
Abdelkrim Nemra ◽  
Oumima Mouali ◽  
Mehdia Hedir ◽  
Abdenebi Rouigueb ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Simultaneous Localization And Mapping ◽  
Mapping Algorithm ◽  
Localization And Mapping ◽  
Aerial Vehicle

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.

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. 

scholarly journals A novel approach for the global localization problem

2012 ◽  
Vol 22 ◽  
pp. 106-112
Author(s):  
Alfredo Toriz ◽  
Abraham Sánchez ◽  
Maria A. Osorio
Keyword(s):  
Data Structure ◽  
Simultaneous Localization And Mapping ◽  
Random Tree ◽  
Localization Problem ◽  
Global Localization ◽  
Mapping Algorithm ◽  
B Splines ◽  
Novel Approach ◽  
Localization And Mapping ◽  
Safe Region

This paper describes a simultaneous planning localization and mapping (SPLAM) methodology focussed on the global localization problem, where the robot explores the environment efficiently and also considers the requisites of the simultaneous localization and mapping algorithm. The method is based on the randomized incremental generation of a data structure called Sensor-based Random Tree, which represents a roadmap of the explored area with an associated safe region. A continuous localization procedure based on B-Splines features of the safe region is integrated in the scheme.

Sign in / Sign up

Export Citation Format

Share Document