What problem does it solve? Raw lidar scan points are noisy and unstructured, making it hard to detect walls, doors, and room boundaries. This Skill turns LaserScan data into clean geometric line segments that higher-level navigation and mapping logic can reason about. ## Core Features & Use Cases - Three Line Extraction Algorithms: Split-and-Merge for fast structured indoor scans, Hough Transform for noisy or gapped scenes, and RANSAC for high outlier robustness, with a comparison table to guide selection. - ROS Integration Guidance: Ready-to-use parameters, launch snippet, and output topics for the laser_line_extraction ROS package publishing LineSegmentList and RViz markers. - Application Patterns: Recipes for door detection via collinear segment gaps, room segmentation from connected wall lines, and corridor-following by aligning the robot to the nearest wall segment. - Use Case: A robot navigating an indoor corridor uses extracted wall lines to compute its lateral offset and yaw relative to the nearest wall, feeding a controller for precise corridor following. ## Quick Start Ask the assistant to extract wall lines from the robot's LaserScan data using Split-and-Merge and tune the split threshold for an indoor environment.
