ROS-simulator-practice

ROS practice using MORAI simulator

Overview

The scenario files, sensor files, network configuration files, and weight files for the models used in the ROS packages are available on Google Drive.
These packages primarily focus on perception tasks and are compatible with ROS1 Noetic.

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Directory Structure

ROS-simulator-practice/
├── get_GT/   # Scripts for extracting GT data
├── pedestrian_detection/    # ROS package folder for object detection
├── traffic_classification/     # ROS package folder for image classification

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Directory Details

get_GT

This folder contains scripts for extracting ground truth (GT) data from the simulator:

• capture_sensor_data.py: Leverages the sensor capture feature of the MORAI simulator.
• save_traffic_img.py: Subscribes to traffic information topics, organizes traffic data into folders categorized by traffic classes, and saves the images for classification tasks.

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pedestrian_detection

This is a ROS package:

• Preparation:
  • Before running, place the ped_weight.pt file (download from Google Drive) into the src folder.
• Functionality:
  • The bbox_publisher.py script subscribes to image topics, processes them using a YOLO model, and publishes real-time bounding box information.

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traffic_classification

This is another ROS package:

• Preparation:
  • Before running, place the traffic_weight.pt file (download from Google Drive) into the src folder.
• Functionality:
  • The traffic_publisher.py script subscribes to image topics, processes them using the AlexNet model, and publishes real-time traffic classification information.

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How to Use ROS Packages

Below is an example for using the pedestrian_detection package:

1. Copy the package to your ROS workspace:

   cp -r pedestrian_detection/ {your_ros_workspace}/src

2. Build the package:

   cd {your_ros_workspace} && catkin_make && source devel/setup.bash

3. Launch the package:

   roslaunch pedestrian_detection run.launch

The same workflow applies to the traffic_classification package.

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