scholarly journals Profiling NVIDIA Jetson Embedded GPU Devices for Autonomous Machines

2020 ◽  
Author(s):  
Yazhou Li ◽  
Yahong Rosa Zheng
Keyword(s):  
Effective Means ◽  
Field Testing ◽  
Operating Mode ◽  
Autonomous Driving ◽  
Robot Operating System ◽  
Race Car ◽  
Localization And Mapping ◽  
Autonomous Machines ◽  
Particle Filter Algorithm ◽  
Embedded Gpu

This paper presents two methods, tegrastats GUI version jtop and Nsight Systems, to profile NVIDIA Jetson embedded GPU devices on a model race car which is a great platform for prototyping and field testing autonomous driving algorithms. The two profilers analyze the power consumption, CPU/GPU utilization, and the run time of CUDA C threads of Jetson TX2 in five different working modes. The performance differences among the five modes are demonstrated using three example programs: vector add in C and CUDA C, a simple ROS (Robot Operating System) package of the wall follow algorithm in Python, and a complex ROS package of the particle filter algorithm for SLAM (Simultaneous Localization and Mapping). The results show that the tools are effective means for selecting operating mode of the embedded GPU devices.

F1_Tenth_Course_Report_Group_C

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.

Comparison of different SLAM approaches for a driverless race car

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Nick Le Large ◽  
Frank Bieder ◽  
Martin Lauer
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Ground Truth ◽  
Time Constraints ◽  
Differential Gps ◽  
Real World Data ◽  
Race Car ◽  
Localization And Mapping ◽  
Slam Algorithm ◽  
Computational Resources

Abstract For the application of an automated, driverless race car, we aim to assure high map and localization quality for successful driving on previously unknown, narrow race tracks. To achieve this goal, it is essential to choose an algorithm that fulfills the requirements in terms of accuracy, computational resources and run time. We propose both a filter-based and a smoothing-based Simultaneous Localization and Mapping (SLAM) algorithm and evaluate them using real-world data collected by a Formula Student Driverless race car. The accuracy is measured by comparing the SLAM-generated map to a ground truth map which was acquired using high-precision Differential GPS (DGPS) measurements. The results of the evaluation show that both algorithms meet required time constraints thanks to a parallelized architecture, with GraphSLAM draining the computational resources much faster than Extended Kalman Filter (EKF) SLAM. However, the analysis of the maps generated by the algorithms shows that GraphSLAM outperforms EKF SLAM in terms of accuracy.

Development of Control System for International Autonomous Driving Competition Using Robot Operating System

2019 ◽  
Vol 25 (5) ◽  
pp. 363-373 ◽  
Author(s):  
Jong-Hang Lee ◽  
Kyung-Jae Ahn ◽  
Taek-Gyu Lee ◽  
Kyung-In Min ◽  
Oh-Sung Kwon ◽  
...  
Keyword(s):  
Operating System ◽  
Control System ◽  
Autonomous Driving ◽  
Robot Operating System

scholarly journals Developing a real time navigation for the mobile robots at unknown environments

2020 ◽  
Vol 20 (1) ◽  
pp. 500
Author(s):  
Sarah Haider Abdulredah ◽  
Dheyaa Jasim Kadhim
Keyword(s):  
Mobile Robot ◽  
Mean Square ◽  
Matlab Simulation ◽  
Robot Operating System ◽  
Unknown Environments ◽  
Localization And Mapping ◽  
Software Platforms ◽  
The Mean ◽  
Simulation Results ◽  
Robot Platform

<p><span>This research deals with the feasibility of a mobile robot to navigate and discover its location at unknown environments, and then constructing maps of these navigated environments for future usage. In this work, we proposed a modified Extended Kalman Filter- Simultaneous Localization and Mapping (EKF-SLAM) technique which was implemented for different unknown environments containing a different number of landmarks. Then, the detectable landmarks will play an important role in controlling the overall navigation process and EKF-SLAM technique’s performance. MATLAB simulation results of the EKF-SLAM technique come with better performance as compared with an odometry approach performance in terms of measuring the mean square error, especially when increasing the number of landmarks. After that, we simulate and evaluate a mobile robot platform named TurtleBot2e in Gazebo simulator software to achieve the using of the SLAM technique for a different environment using the Rviz library which was built on Robot Operating System in Linux. The main conclusion comes with this work is the simulation and implementation of the SLAM technique using two software platforms separately (MATLAB and ROS) in different unknown environments containing a different number of landmarks so a few number of landmark will make the mobile robot loses its path.</span></p>

scholarly journals Hololens Used for Precise Position Tracking of the Third Party Devices - Autonomous Vehicles

2019 ◽  
Vol 21 (2) ◽  
pp. 18-23
Author(s):  
Jindrich Cyrus ◽  
David Krcmarik ◽  
Reza Moezzi ◽  
Jan Koci ◽  
Michal Petru
Keyword(s):  
Autonomous Vehicles ◽  
Complex Space ◽  
Autonomous Driving ◽  
Real Space ◽  
Third Party ◽  
Location Information ◽  
Position Tracking ◽  
Precise Position ◽  
The Third ◽  
Localization And Mapping

A completely new area of HoloLens usage is proposed. The Hololens is an augmented reality device, which provides the high precision location information. Such an information is normally used to accurately position holograms within the real space with respect to the viewer (user of HoloLens). The information is precise enough to use it for reporting the position for the purpose of autonomous driving. Several experiments have been executed in vast areas (20 m x 40 m) in order to find out the potential error coming from vibrations or other effects when moving the HoloLens. The results show that the technology can be used for spaces, which are previously known by the system - pre-scan of the space is needed. The big advantage of the system is its readiness for indoor positioning applications with no additional infrastructure needed, simultaneous localization and mapping, complex space mapping and reached precision. The disadvantage is mainly the costs.

scholarly journals FAULT-TOLERANT DESIGN OF A GEAR SHIFTING SYSTEM FOR AUTONOMOUS DRIVING

2020 ◽  
Vol 1 ◽  
pp. 1125-1134 ◽  
Author(s):  
R. Stetter ◽  
R. Göser ◽  
S. Gresser ◽  
M. Witczak ◽  
M. Till
Keyword(s):  
Fault Tolerant ◽  
Autonomous Driving ◽  
Gear System ◽  
Race Car ◽  
Technical Systems ◽  
Fault Tolerant Design

AbstractThis paper reports the application of the methods and tools of fault-tolerant design to an automated shifting system and their reflection and extension. Fault-tolerant design has emerged in the last years and is generally understood as a collection of strategies, methods, algorithms, tools and insights which are intended to support the development of technical systems which are fault-tolerant because of their controllability but also their inherent fault-tolerant design qualities. The field of application is a shifting system for the gear system of a formula student driverless race car.

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