ROS Lane Detection using Least Squares and Feature Estimation

This project demonstrates a lane detection system for autonomous vehicles using Least Squares (LS) fitting techniques, combined with basic curve recognition. The implementation was done in Python and ROS, using simulated sensor inputs and data visualization.

🚗 Project Goals

• Detect and track lane lines using camera-like simulated input
• Apply least squares estimation (LS) for curve fitting
• Use MSB/LSB bit logic for precise sensor data analysis
• Visualize results in iterative image loops

📚 Technical Highlights

• ROS-based simulation for autonomous driving
• Python scripts for linear acceleration and steering control
• Analysis of left and right lane point clouds (pts_left.npy, pts_right.npy)
• Live visualization using matplotlib

🧠 Algorithms Used

• Least Squares Estimation
• Curve Detection and Matching
• Bit-Level Interpretation (MSB/LSB)
• Feature Visualization

📸 Example Outputs

Step	Preview
Step 20
Step 60
Step 80

🎯 Applications

• Lane Keeping Assist (LKA)
• Autonomous Navigation
• Robotics & Intelligent Systems

📎 Related Projects

• Lidar Object Tracking
• LDS Clustering and Detection
• YouTube Demo Video

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Author: Halit Osman Efkere
Affiliation: Hochschule Esslingen
Course: Autonomous Systems – Lane Detection Lab (Wintersemester 2024/2025)
Focus: Autonomous Systems, Embedded Software, ROS, Sensor Fusion