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Program Launch Guide

This guide provides step-by-step instructions to launch the ROS assessment program.


Prerequisites

Before starting, ensure you have completed Step 3 (Build & Source) at least once.


Launch Steps

Step 1: Launch Gazebo Simulation

Choose one of the following commands:

Option A:

ros2 launch limo_gazebosim limo_gazebo_diff.launch.py world:=src/ros_assesment/worlds/home.world

Option B:

ros2 launch ros_assesment sim_with_world.launch.py

📝 Note: This step 3 must be completed before using Option B.


Step 2: Launch Navigation

ros2 launch limo_navigation limo_navigation.launch.py map:=src/ros_assesment/maps/my_map.yaml use_sim_time:=true

⚠️ Important: Add the Depth Camera topic in RVIZ before proceeding to the next step.


Step 3: Build & Source Package

colcon build --packages-select ros_assesment --symlink-install
source install/setup.bash
ros2 run ros_assesment <name of py>

Step 4: Launch Object Detection

ros2 run find_object_2d find_object_2d --ros-args \
    -r image:=/limo/depth_camera_link/image_raw \
    -p objects_path:=/workspaces/cmp9767-Robotics-Edosa-module/src/ros_assesment/trained_objects \
    -p gui:=false

Step 5: Run 3D Object Finder

ros2 run ros_assesment find_object_to_3d

Step 6: Run 3D Counter

ros2 run ros_assesment counter_3d

Step 7: Configure Costmap Parameters

Launch RQT and adjust the following parameters:

rqt

Configuration Settings:

  • Local costmap scaling factor: 1.35
  • Inflation radius: 0.35

Step 8: Run Waypoint Automation

ros2 run ros_assesment automate_waypoints

Quick Reference

Step Command Description
1 ros2 launch limo_gazebosim... Launch Gazebo world
2 ros2 launch limo_navigation... Start navigation stack
3 colcon build... Build and source package
4 ros2 run find_object_2d... Start object detection
5 ros2 run ros_assesment find_object_to_3d Convert to 3D coordinates
6 ros2 run ros_assesment counter_3d Run 3D counter
7 rqt Configure costmap
8 ros2 run ros_assesment automate_waypoints Start automation

About

ROS2 + Nav2 mobile robot that autonomously navigates indoor spaces and detects, 3D-localises and counts safety equipment using SIFT + RGB-D fusion. 95.8% detection, F1 0.95.

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