Behavior:SLAM

SLAM
Type	Behavior (Algorithm)
Requires Capabilities	Capability:LIDAR Sensing or Capability:Camera Vision, Capability:Optical Odometry, Capability:Differential Drive
Enables Activities	Activity:Room Mapping, Activity:Maze Optimization, autonomous navigation
Difficulty	Advanced
Status	Stub - Algorithm not yet implemented

SLAM is a behavior (algorithm) that simultaneously builds a map of an environment while tracking the robot's position within it.

Overview

This is a stub page. This behavior is not yet implemented in any BRS robot. This page exists to:

• Document the algorithmic concept
• Invite community members to implement it
• Provide a starting point for algorithm design

Required Capabilities

This behavior requires:

• Capability:LIDAR Sensing
• Capability:Camera Vision
• Capability:Optical Odometry
• Capability:Differential Drive

Enables Activities

Implementing this behavior enables:

• Activity:Room Mapping
• Activity:Maze Optimization

Algorithm Outline

SLAM is computationally intensive and typically uses established algorithms:

• • 2D LIDAR SLAM**:
• Gmapping
• Hector SLAM
• Cartographer

• • Visual SLAM**:
• ORB-SLAM
• LSD-SLAM

General approach:

1. Capture sensor data (LIDAR scan or camera image)
2. Extract features/landmarks
3. Match to previously seen features
4. Estimate robot motion (odometry + sensor matching)
5. Update map with new observations
6. Correct for loop closure (detecting return to known location)

Pseudocode

# Simplified SLAM Concept (not implementable as-is)
map = initialize_empty_map()
robot_pose = (0, 0, 0)  # x, y, theta

while True:
    sensor_data = capture_lidar_scan()
    odometry_delta = read_odometry()

    # Predict new pose from odometry
    predicted_pose = robot_pose + odometry_delta

    # Match sensor data to map
    matched_features = feature_matching(sensor_data, map)

    # Correct pose estimate based on matches
    corrected_pose = optimize_pose(predicted_pose, matched_features)

    # Update map with new observations
    update_map(map, corrected_pose, sensor_data)

    robot_pose = corrected_pose

Implementation Challenges

• **Computational cost**: Requires significant processing power (use ROS on Raspberry Pi)
• **Loop closure**: Detecting when robot returns to known location
• **Data association**: Matching current observations to previous ones
• **Scale**: Large maps require sophisticated data structures
• **Recommended**: Use existing SLAM libraries (ROS navigation stack) rather than implementing from scratch

Contributing

Want to implement this behavior? Here's how:

1. Study the algorithm outline above
2. Implement in your language of choice (MicroPython, C++, Arduino)
3. Test on a robot with the required capabilities
4. Create an Implementation page (e.g., YourRobot:SLAM Implementation)
5. Update this page with algorithm refinements
6. Share working code on GitHub

See Also

• Behaviors - All behaviors
• Capabilities - Hardware required
• Activities - What this enables
• Robotics Ontology - How behaviors fit into BRS knowledge structure