John: Claude edited based on my notes, prompt, and SimpleBot code repository

2025-10-11T16:50:46Z

Claude edited based on my notes, prompt, and SimpleBot code repository

New page

{{Behavior
| name = SLAM
| requires = [[Capability:LIDAR Sensing]] or [[Capability:Camera Vision]], [[Capability:Optical Odometry]], [[Capability:Differential Drive]]
| enables = [[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:
# Capture sensor data (LIDAR scan or camera image)
# Extract features/landmarks
# Match to previously seen features
# Estimate robot motion (odometry + sensor matching)
# Update map with new observations
# Correct for loop closure (detecting return to known location)

== Pseudocode ==

<pre>
# 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
</pre>

== 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:

# Study the algorithm outline above
# Implement in your language of choice (MicroPython, C++, Arduino)
# Test on a robot with the required capabilities
# Create an Implementation page (e.g., <code>[[YourRobot:SLAM Implementation]]</code>)
# Update this page with algorithm refinements
# 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

[[Category:Behavior]]
[[Category:Advanced Behavior]]
[[Category:Stub]]

Behavior:SLAM - Revision history

John: Claude edited based on my notes, prompt, and SimpleBot code repository