multi_sensor_slam_ws

Multi-Sensor SLAM System for ROS2 - Integrating LiDAR, Camera, IMU, and GPS

Overview

This workspace contains a ROS2-based SLAM (Simultaneous Localization and Mapping) system that fuses data from multiple sensors to create accurate maps and localization.

Current Status

✅ Completed

• ROS2 Workspace Setup: Initialized workspace structure for ROS2 Humble
• Core Package Structure: Created three main packages:
  • multi_sensor_slam_core: Main SLAM implementation with TF2 and navigation support
  • multi_sensor_slam_lidar: LiDAR data processing node
  • multi_sensor_slam_camera: Camera/image processing with OpenCV bridge
• Build System: All packages build successfully with colcon
• Dependencies: Configured with essential ROS2 packages (sensor_msgs, geometry_msgs, nav_msgs, tf2, etc.)

🚧 In Progress

• Sensor Calibration: Investigating koide3's direct_visual_lidar_calibration
  • Currently facing dependency version conflicts (Eigen/GTSAM compatibility)
  • Package temporarily excluded from build until resolved

Planned Features

Short Term

1. Sensor Calibration Pipeline

   • Integrate direct_visual_lidar_calibration once build issues are resolved
   • Implement automatic extrinsic calibration between sensors

2. Sensor Drivers

   • LiDAR driver integration (Velodyne/Ouster/Livox support)
   • Camera driver setup with proper intrinsic calibration
   • IMU and GPS driver configuration

3. Basic SLAM Implementation

   • Point cloud preprocessing and filtering
   • Feature extraction from LiDAR and camera data
   • Initial odometry estimation

Medium Term

1. Sensor Fusion

   • Tightly-coupled LiDAR-Visual-Inertial odometry
   • GPS integration for global localization
   • Multi-sensor data synchronization

2. Mapping

   • 3D point cloud map generation
   • Occupancy grid mapping for navigation
   • Loop closure detection and optimization

3. Visualization

   • RViz2 configuration for real-time visualization
   • Web-based monitoring interface

Long Term

1. Advanced Features

   • Dynamic object detection and removal
   • Semantic mapping capabilities
   • Multi-robot SLAM support

2. Optimization

   • GPU acceleration for point cloud processing
   • Real-time performance optimization
   • Memory-efficient map management

3. Applications

   • Autonomous navigation integration
   • AR/VR applications with accurate localization
   • Integration with popular robotics frameworks

Dependencies

• ROS2 Humble
• PCL (Point Cloud Library)
• OpenCV
• Eigen3
• GTSAM (for optimization)
• Ceres Solver (for calibration)

Building

cd ~/multi_sensor_slam_ws
colcon build --symlink-install
source install/setup.bash

References

• koide3's SLAM packages - Excellent point cloud registration and calibration tools
• direct_visual_lidar_calibration - Target-less LiDAR-camera calibration