ira_laser_tools
carla
| ira_laser_tools | carla | |
|---|---|---|
| 1 | 22 | |
| 188 | 10,516 | |
| 0.0% | 1.6% | |
| 0.0 | 9.4 | |
| over 1 year ago | 4 days ago | |
| C++ | C++ | |
| BSD 3-clause "New" or "Revised" License | MIT License |
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ira_laser_tools
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What are your questions and pain points in ROS2?
I found the ROS2 concept and design much more well established, especially for industrial. My problem on porting was more like when something available in ROS1 concept is not supported in the ROS2 concept. For example the infamous ira_laser_tools. This package basically merges all the scanning laser topics and publishes it as one so we can use it for AMCL / navigation (with the PointClouds too). The package will use ros::master api so it can manipulate the value alongside the data type format in which such concept is not supported in ROS2. In ROS1 we can take a list of topics with the data types in the format of std::string (topic name), std::string (data type). Laser topic parser in this package will process the data in a c++ way to get only the ones publishing sensor_msgs (scan data type). We can also call topics and the data type in ROS2, but in a format of std::map... I don't have sufficient c++ knowledge and reference to parse from the ROS2 api. I believe these kind of difficulties is also why nobody is working on it too.. In cases like these, how do you tackle the issue?
carla
- Tesla braces for its first trial involving Autopilot fatality
- Mediocre Arduino Coder here: is there anyone that can offer their expertise on a virtual autonomous car project?
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Best Self Driving Cars Projects.
It sounds like you're looking for something like the CARLA simulator.
- What good Autonomous Driving simulators for research?
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Importing map from google maps
If you are looking for a different simulator, I would suggest using (Carla)[https://carla.org/] with ROS bridge and it also has an inbuilt support for OSM which worked flawlessly (you have to install it from source to get the OSM plugin).
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[D] Doing my (bachelor) thesis on RL. Which topic do you like best?
(3) I would suggest you use CARLA or TORCS for self-driving cars in RL as they are common test beds.
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Can someone build carla RL env for centOS for me?
carla env
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Car simulation RL environment - Carla centOS build
Link to carla
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Best way to simulate and train VSLAM based robot in virtual environments?
There are a few ways you could go with this. A fun recent trend has been to make simulators in the unreal engine for photorealistic training. If you wanted to spend your whole project on the simulator part you could make your own environment, but I highly recommend using an open-source sim package. If you don't care too much about photorealism, you could use gazebo just fine. I've also made small visual worlds in blender, then simulated with RVIZ. Here's a good one with a focus on aerial robotics: https://theairlab.org/tartanair-dataset/ You could also give CARLA a spin for autonomous vehicles: https://carla.org/
- Open-source simulator for autonomous driving research
What are some alternatives?
PlotJuggler - The Time Series Visualization Tool that you deserve.
AirSim - Open source simulator for autonomous vehicles built on Unreal Engine / Unity, from Microsoft AI & Research
PCL - Point Cloud Library (PCL)
simulator - A ROS/ROS2 Multi-robot Simulator for Autonomous Vehicles
micro_ros_raspberrypi_pico_sdk - Raspberry Pi Pico (RP2040) and micro-ROS integration
openpilot - openpilot is an open source driver assistance system. openpilot performs the functions of Automated Lane Centering and Adaptive Cruise Control for 250+ supported car makes and models.
PX4-Autopilot - PX4 Autopilot Software
apollo - An open autonomous driving platform
ardupilot - ArduPlane, ArduCopter, ArduRover, ArduSub source
webots - Webots Robot Simulator
gym - A toolkit for developing and comparing reinforcement learning algorithms.
ml-agents - The Unity Machine Learning Agents Toolkit (ML-Agents) is an open-source project that enables games and simulations to serve as environments for training intelligent agents using deep reinforcement learning and imitation learning.