ROS-Based SLAM for a Gazebo-Simulated Mobile Robot in Image-Based 3D Model of Indoor Environment

2015 ◽  
pp. 273-283 ◽  
Author(s):  
Ilya Afanasyev ◽  
Artur Sagitov ◽  
Evgeni Magid
Keyword(s):  
Mobile Robot ◽  
Indoor Environment ◽  
3D Model

Simulation of a Differential-Drive Wheeled Mobile Lego Robot Mindstorms NXT

2015 ◽  
Vol 776 ◽  
pp. 319-324
Author(s):  
I. Wayan Widhiada ◽  
C.G. Indra Partha ◽  
Yuda A.P. Wayan Reza
Keyword(s):  
Mobile Robot ◽  
3D Model ◽  
Hardware Implementation ◽  
Movement Control ◽  
Common Environment ◽  
Matlab Simulink ◽  
Differential Drive ◽  
Simulation Results ◽  
Mechanical Elements ◽  
Drive Model

The aim of this paper is to model and simulate kinematics motion using the differential drive model of a mobile Lego robot Mindstorm NXT. The author’s use integrated two software as a method to solve the simulation of mobile lego robot mindstorms NXT using Matlab/Simulink and Solidworks software. These softwares are enable easier 3D model creation for both simulation and hardware implementation. A fundamental of this work is the use of Matlab/Simulink Toolboxes to support the simulation and understanding of the various kinematics systems and in particular how the SimMechanics toolbox is used to interface seamlessly with ordinary Simulink block diagrams to enable the mechanical elements and its associated control system elements to be investigated in one common environment. The result of simulation shows the mobile robot movement control based on decentralized point algorithm to follow the precision x and y references that has been specified. The design of the mobile robot is validated in simulation results as proof that this design can achieve the good performance.

scholarly journals Benchmark Dataset Based on Category Maps with Indoor–Outdoor Mixed Features for Positional Scene Recognition by a Mobile Robot

Robotics ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 40
Author(s):  
Hirokazu Madokoro ◽  
Hanwool Woo ◽  
Stephanie Nix ◽  
Kazuhito Sato
Keyword(s):  
Mobile Robot ◽  
Visual Information ◽  
Indoor Environment ◽  
Scene Recognition ◽  
Visual Features ◽  
Wide Field ◽  
Mixed Features ◽  
Wide Field Of View ◽  
Benchmark Datasets ◽  
Unified Method

This study was conducted to develop original benchmark datasets that simultaneously include indoor–outdoor visual features. Indoor visual information related to images includes outdoor features to a degree that varies extremely by time, weather, and season. We obtained time-series scene images using a wide field of view (FOV) camera mounted on a mobile robot moving along a 392-m route in an indoor environment surrounded by transparent glass walls and windows for two directions in three seasons. For this study, we propose a unified method for extracting, characterizing, and recognizing visual landmarks that are robust to human occlusion in a real environment in which robots coexist with people. Using our method, we conducted an evaluation experiment to recognize scenes divided up to 64 zones with fixed intervals. The experimentally obtained results using the datasets revealed the performance and characteristics of meta-parameter optimization, mapping characteristics to category maps, and recognition accuracy. Moreover, we visualized similarities between scene images using category maps. We also identified cluster boundaries obtained from mapping weights.

Robust Vision-Based Monitoring of Indoor Environments by an Autonomous Mobile Robot

2007 ◽  
Author(s):  
Donato Di Paola ◽  
Annalisa Milella ◽  
Grazia Cicirelli ◽  
Arcangelo Distante
Keyword(s):  
Mobile Robot ◽  
Indoor Environment ◽  
Experimental Tests ◽  
Indoor Environments ◽  
Autonomous Mobile Robot ◽  
Environment Monitoring ◽  
Inference System ◽  
Sift Algorithm ◽  
Matching Process ◽  
Logic Inference

This paper presents a novel vision-based approach for indoor environment monitoring by a mobile robot. The proposed system is based on computer vision methods to match the current scene with a stored one, looking for new or removed objects. The matching process uses both keypoint features and colour information. A PCA-SIFT algorithm is employed for feature extraction and matching. Colour-based segmentation is performed separately, using HSV coding. A fuzzy logic inference system is applied to fuse information from both steps and decide whether a significant variation of the scene has occurred. Results from experimental tests demonstrate the feasibility of the proposed method in robot surveillance applications.

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