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Here’s a clean, technical README summary you can directly use (or slightly tweak) for your ROS 2 package:

🧭 Obstacle Detector (ROS 2)

Overview

obstacle_detector is a lightweight ROS 2 package for real-time obstacle detection and classification using 2D LiDAR data (sensor_msgs/LaserScan). It processes incoming scan data, filters noise, groups nearby points into clusters, and categorizes obstacles into safety zones based on distance.

The node publishes both visualization markers for RViz and a high-level obstacle state for downstream decision-making (e.g., navigation, control).

✨ Features

  • πŸ“‘ Subscribes to LiDAR scan data (/scan)
  • 🧹 Applies moving-average filtering to reduce noise
  • πŸ“ Converts polar scan data β†’ Cartesian coordinates
  • 🧠 Classifies obstacles into:
    • CRITICAL
    • MODERATE
    • FRIENDLY
    • CLEAR
  • πŸ”— Performs simple spatial clustering of obstacle points
  • 🎨 Publishes clustered obstacles as RViz markers
  • πŸ“’ Publishes system state for behavior/control modules

πŸ—οΈ Node Architecture

Node: obstacle_detector

Subscriptions

TopicTypeDescription
/scansensor_msgs/msg/LaserScanInput LiDAR data

Publications

TopicTypeDescription
/obstacle_clustersvisualization_msgs/msg/MarkerClustered obstacle visualization
/obstacle_modestd_msgs/msg/StringCurrent obstacle state

βš™οΈ Parameters

ParameterTypeDescriptionDefault
rover_lengthfloatRobot length (m)0.7
rover_widthfloatRobot width (m)0.5
lidar_offset_xfloatLiDAR X offset0.0
lidar_offset_yfloatLiDAR Y offset0.0
critical_radiusfloatImmediate danger zone0.7
moderate_radiusfloatCaution zone1.5
friendly_radiusfloatAwareness zone2.0

Processing Pipeline

1. Noise Filtering

A sliding window (moving average) is applied to smooth LiDAR range data:

  • Window size: Β±2 samples
  • Ignores NaN and inf values

2. Coordinate Transformation

Each valid scan point is converted:

  • Polar β†’ Cartesian
x = r * cos(ΞΈ)
y = r * sin(ΞΈ)

3. Distance-Based Classification

Points are categorized into zones:

  • Critical: r ≀ critical_radius
  • Moderate: critical_radius < r ≀ moderate_radius
  • Friendly: moderate_radius < r ≀ friendly_radius

4. Clustering

A simple proximity-based clustering algorithm:

  • Threshold: 0.4 m
  • Groups nearby points into obstacle clusters
  • Computes:
    • Cluster centroid
    • Distance and angle
    • Cluster size

5. Visualization

  • Uses Marker::SPHERE_LIST
  • Each cluster gets a unique color
  • Published to /obstacle_clusters for RViz

6. Mode Estimation

The system publishes a global state:

Priority-based logic:

CRITICAL > MODERATE > FRIENDLY > CLEAR

Example:

  • Any critical point β†’ CRITICAL
  • Else if moderate β†’ MODERATE
  • Else if friendly β†’ FRIENDLY
  • Else β†’ CLEAR

Launch

ros2 launch obstacle_detector detector_launch.py

Dependencies

  • rclcpp
  • sensor_msgs
  • visualization_msgs
  • std_msgs

Example Use Cases

  • Autonomous rover safety monitoring
  • Reactive obstacle avoidance
  • Navigation stack augmentation
  • Visualization/debugging of LiDAR perception

Limitations

  • Uses a naive clustering algorithm (not DBSCAN or Euclidean clustering)
  • No temporal tracking of obstacles
  • No dynamic obstacle prediction
  • Assumes relatively clean LiDAR input

Future Improvements

  • Replace clustering with DBSCAN / PCL
  • Add obstacle tracking over time
  • Integrate velocity estimation
  • Fuse with camera/depth sensors
  • Adaptive thresholds based on speed