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  • 2022-05-27 13:16
matlab分时代码方程式学生无人驾驶资源 该存储库专用于在所有Formula Student无人驾驶团队以及开发用于自动驾驶和赛车的软件和硬件的所有其他感兴趣的部件之间共享资源。 从这里叉: 目录: 数据集 本节专门分享在方程式学生无人驾驶汽车中收集的数据或与之相关的数据。 AMZ无人驾驶2017数据集 数据可以在这里找到 数据采用.bag格式。 这是ROS的标准日志记录格式,可以使用可用的工具轻松将其导入到matlab中。 轿厢参考系定义为(前,左,上),其原点位于IMU参考系中。 所有传感器的数据已经与汽车对齐。 除非另有说明,否则数据以国际系统单位给出。 数据是在苏黎世郊区的机场归档的。 地面大部分是平坦的,但有一些弯曲的区域和颠簸。 路的一侧有高高的草丛。 那天很晴朗。 在这个rosbag中,您可以找到以下主题: velodyne_points:包含以sensor_msgs / PointCloud2消息类型返回的激光雷达点。 激光雷达是Velodyne Puck VLP16,消息是传感器发送的单个数据包。 LiDAR在car_frame中的位置为x = 1.6 m,y =
  • FSD-Resource-master
# Formula Student Driverless Resources This repository is devoted to share resources among all Formula Student Driverless teams as well as all other interested parts developing Software and Hardware for autonomous driving and racing. Fork from this:[AMZ fsd-resources]( ## Table of Contents: - [Datasets](#datasets) - [AMZ Driverless 2017 Dataset](#amz_driverless_2017) - [AMZ Vision 2017 Dataset ](#amz_vision_2017) - [municHMotorsport Formula Student Objects in Context (FSOCO)](#munichmotorsports_fsoco) - [BIT Driverless 2018 DataSet](#bit_vision_2018) - [SW Tools](#sw_tools) - [Rosbag Bazaar](#rbb) - [Algorithms](#algorithms) - [MIT Driverless Computer Vision](#mitcv) - [MPCC](#mpcc) - [Global Race Trajectory Optimization](#grto) - [FSD skeleton](#skeleton) - [Python Robotics](#pythonrobotics) - [BIT FSD Algorithm](#bitalog) - [Simulations](#sims) - [EUFS Simulation](#eufs_sim) - [FSSIM](#fssim) - [Conference Papers & Journal Articles ](#papers) - [Reports](#reports) - [Presentations](#presentations) - [Videos](#videos) - [Link](#link) ___ <br> <a name="datasets" rel='nofollow' onclick='return false;'></a> # Datasets This section is devoted to share data collected in, or related to, Formula Student Driverless Vehicles. <a name="amz_driverless_2017" rel='nofollow' onclick='return false;'></a> ## AMZ Driverless 2017 Dataset - The data can be found in this [link]( - The data comes in .bag format. This is the standard logging format for ROS and it can be easily imported to matlab using available tools. - The car reference frame is defined as (front, left, up) and has its origin in the IMU reference frame. All the sensors' data are already aligned with the car. The data is given in International Sytem Units unless otherwise specified. - The data was collected in an airfiled in the outskirts of Zurich. The ground is mostly flat but there are some curved regions and bumps. There is high grass on the side of the of the road. The day was very sunny. - In this rosbag one can find the following topics: - velodyne_points : Contains the Lidar point returns in a sensor_msgs/PointCloud2 message type. The Lidar is a Velodyne Puck VLP16 and the message are the individual packets sent by the sensor. The position of the LiDAR in car_frame is x= 1.6 m, y= 0.0 m. - optical_speed_sensor: Contains ground speed data in a geometry_msgs/TwistStamped message type. This sensor is a Kistler Correvit SFII. The position of this sensor in the car frame is x= -0.41m y= 0.27 m. - wheel_rpm : contains the wheel speed data in a geometry_msgs/QuaternionStamped message type. x -> front left wheel, y -> front right wheel. z -> rear left wheel. w -> rear right wheel. The data is expressed in rpm's. The distance to thefront axel is 0.81m, to the rear axel 0.72m, and the track width is 1.2m. - imu : Contains the accelerometer and gyroscopes information in a sensor_msgs/Imu message type. This sensor is an SBG ellipse-N. The position of this sensor in the car frame is x = 0.0 m, y = 0.0 m. - gps : Contains the GPS information in sensor_msgs/NavSatFix message type. The data is expressed in degrees for Lattitue and Longitude. The sensor is the same SBG ellipse-N used as IMU. <a name="amz_vision_2017" rel='nofollow' onclick='return false;'></a> ## AMZ Vision 2017 Dataset - A Vision dataset taken on fluela driverless with all the details can be found in this [link]( <a name="munichmotorsports_fsoco" rel='nofollow' onclick='return false;'></a> ## municHMotorsport Formula Student Objects in Context (FSOCO) - Open-Source Dataset for Objects that need to be recognized during the dynamic disciplines of the Formula Student Driverless competitions, started by municHMotorsports, [link here]( <a name="bit_vision_2018" rel='nofollow' onclick='return false;'></a> ## BIT Driverless 2018 DataSet - VOC formal, all the details can be found in this [link]( <a name="sw_tools" rel='nofollow' onclick='return false;'></a> # SW Tools This section is devoted to share software tools related to or helpful in, Formula Student Driverless. <a name="rbb" rel='nofollow' onclick='return false;'></a> ## Rosbag Bazaar - The Rosbag Bazaar (RBB) is a tool to index/visualize/manage rosbags on remote storage systems. Additionally it provides a web interface and framework for automated simulations. It is a tool helpful to anyone handling large amounts of rosbags and complex software pipelines in real robots. Find the code in this [link]( <a name="algorithms" rel='nofollow' onclick='return false;'></a> # Algorithms This section is devoted to share Algorithms dealing with or related to, Formula Student Driverless Vehicles. They could be Visual pipelines, Lidar, estimation, control, etc.. <a name="mitcv" rel='nofollow' onclick='return false;'></a> ## MIT Driverless Compuer Vision - Pytorch pipeline of MIT Driverless Computer Vision. Find all the details in this [link]( <a name="mpcc" rel='nofollow' onclick='return false;'></a> ## MPCC - Model Predictive Contouring Controller (MPCC) for Autonomous Racing developed by the Automatic Control Lab (IfA) at ETH Zurich. Find all the details in this [link]( <a name="grto" rel='nofollow' onclick='return false;'></a> ## Global Race Trajectory Optimization - Global Race Trajectory Optimization developed by the chair of Automotive Technology of Technical University of Munich (TUM). Find all the details in this [link]( <a name="skeleton" rel='nofollow' onclick='return false;'></a> ## FSD skeleton The FSD skeleton repository, found [here](, is an example framework for the code used on a FSD race car. Based on the autonomous software of fluela and gotthard driverless, the framework is built in ROS, and contains the structure and basic ROS nodes to illustrate how to organise an autonomous software stack. Some features are: - Easy build management - Custom aliases - Launchfiles for FSD missions - Dependency management <a name="pythonrobotics" rel='nofollow' onclick='return false;'></a> ## Python Robotics - Python code collection of robotics algorithms, especially for autonomous navigation. A great starting point to explore the main algorithms relevant to mobile robotics and Formula Student Driverless. Find all the details in this [link]( <a name="bitalog" rel='nofollow' onclick='return false;'></a> ## BIT FSD Algorithm - This repository is devoted to share the autonomous code of Beijing Institute of Technology Driverless Racing Team. Some simple version code of an autonomous FS race car and some helpful tools are included.Find all the details in this [link]( <a name="sims" rel='nofollow' onclick='return false;'></a> # Simulations This section is devoted to sharing simulations dealing with or related to, Formula Student Driverless Vehicles. This could be vehicle dynamic models, environment models, sensors, etc.. <a name="eufs_sim" rel='nofollow' onclick='return false;'></a> ## EUFS Simulation ROS/Gazebo simulation packages for driverless FSAE vehicles. It features a basic RWD Formula Student vehicle model, dynamic event tracks and highly configurable sensor packages. The repository can be found [here]( ## FSSIM AMZ Driverless simulator which can be found [here]( <a name="papers" rel='nofollow' onclick='return false;'></a> # Conference Papers & Journal Articles This section is devoted to share Conference Papers and Journal Articles dealing with or related to, Formula Student Driverless Vehicles. - Real-time 3D Traffic Cone Detection for Autonomous Driving. Ankit Dhall, Dengxin Dai, Luc Van Gool. [link]( - AMZ Driverless: The Full Autonomous Racing System. Juraj Kabzan, Miguel I. Valls, Victor J.F. Reijgwart, Hubertus F.C. Hendrikx, Claas Ehmke, Manish Prajapat, Andreas Bühler, Nikhil Gosala, Mehak Gupta, Ramya Sivanesan, Ankit Dhall, Eugenio Chisari, Napat Karnchanachari, Sonja Brits, Manuel Dangel, Inkyu Sa, Renaud Dubé, Abel Gawel, Mark Pfeiffer, Alexander Liniger, John Lygeros an
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