Corrections and Comments on High-Accuracy Robot Indoor Localization Scheme Based on Robot Operating System Using Visible Light Positioning

For mobile robots and location-based services, precise and real-time positioning is one of the most basic capability, and low-cost positioning solutions are increasingly in demand and have broad market potential. In this paper, we innovatively design a high-accuracy and real-time indoor localization system based on visible light positioning (VLP) and mobile robot. First of all, we design smart LED lamps with VLC and Bluetooth control functions for positioning. The design of LED lamps includes hardware design and Bluetooth control. Furthermore, founded on the loose coupling characteristics of ROS (Robot Operator System), we design a VLP-based robot system with VLP information transmitted by designed LED, dynamic tracking algorithm of high robustness, LED-ID recognition algorithm, and triple-light positioning algorithm. We implemented the VLP-based robot positioning system on ROS in an office equipped with the designed LED lamps, which can realize cm-level positioning accuracy of 3.231 cm and support the moving speed up to 20 km/h approximately. This paper pushes forward the development of VLP application in indoor robots, showing the great potential of VLP for indoor robot positioning.

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F1_Tenth_Course_Report_Group_C

10.31224/osf.io/68bvh ◽

2018 ◽

Author(s):

Yi Chen ◽

Sagar Manglani ◽

Roberto Merco ◽

Drew Bolduc

Keyword(s):

Operating System ◽

Autonomous Driving ◽

Software Tools ◽

Simulation Environment ◽

Robot Operating System ◽

Available Information

In this paper, we discuss several of major robot/vehicle platforms available and demonstrate the implementation of autonomous techniques on one such platform, the F1/10. Robot Operating System was chosen for its existing collection of software tools, libraries, and simulation environment. We build on the available information for the F1/10 vehicle and illustrate key tools that will help achieve properly functioning hardware. We provide methods to build algorithms and give examples of deploying these algorithms to complete autonomous driving tasks and build 2D maps using SLAM. Finally, we discuss the results of our findings and how they can be improved.

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Dynamic Indoor Localization Using Maximum Likelihood Particle Filtering

Sensors ◽

10.3390/s21041090 ◽

2021 ◽

Vol 21 (4) ◽

pp. 1090

Author(s):

Wenxu Wang ◽

Damián Marelli ◽

Minyue Fu

Keyword(s):

Maximum Likelihood ◽

Particle Filtering ◽

Indoor Localization ◽

Real Data ◽

High Accuracy ◽

Classical Particle ◽

Popular Approach ◽

Filtering Method ◽

Essential Idea ◽

Number Of Particles

A popular approach for solving the indoor dynamic localization problem based on WiFi measurements consists of using particle filtering. However, a drawback of this approach is that a very large number of particles are needed to achieve accurate results in real environments. The reason for this drawback is that, in this particular application, classical particle filtering wastes many unnecessary particles. To remedy this, we propose a novel particle filtering method which we call maximum likelihood particle filter (MLPF). The essential idea consists of combining the particle prediction and update steps into a single one in which all particles are efficiently used. This drastically reduces the number of particles, leading to numerically feasible algorithms with high accuracy. We provide experimental results, using real data, confirming our claim.

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A Robot Operating System Framework for Secure UAV Communications

Sensors ◽

10.3390/s21041369 ◽

2021 ◽

Vol 21 (4) ◽

pp. 1369

Author(s):

Hyojun Lee ◽

Jiyoung Yoon ◽

Min-Seong Jang ◽

Kyung-Joon Park

Keyword(s):

Operating System ◽

Unmanned Aerial Vehicles ◽

Ground Control ◽

Unmanned Aerial System ◽

Security Framework ◽

Security Vulnerabilities ◽

Robot Operating System ◽

Security Issues ◽

Aerial Vehicles ◽

Ground Control Station

To perform advanced operations with unmanned aerial vehicles (UAVs), it is crucial that components other than the existing ones such as flight controller, network devices, and ground control station (GCS) are also used. The inevitable addition of hardware and software to accomplish UAV operations may lead to security vulnerabilities through various vectors. Hence, we propose a security framework in this study to improve the security of an unmanned aerial system (UAS). The proposed framework operates in the robot operating system (ROS) and is designed to focus on several perspectives, such as overhead arising from additional security elements and security issues essential for flight missions. The UAS is operated in a nonnative and native ROS environment. The performance of the proposed framework in both environments is verified through experiments.

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