EKF-SLAM and FastSLAM1.0 Evaluation and Visualization

This repository contains implementations of FastSLAM 1.0 and EKF-SLAM on the MRCLAM dataset, together with tools for evaluation and visualization.

Running SLAM with Error Metrics

If you want to compute and visualize error metrics (ATE, RPE, and landmark RMSE), you should run:

• fastslam_known_correspondences
• ekf_known_correspondences

These scripts:

• Run the full SLAM pipeline
• Compare estimated trajectories and maps against ground truth
• Produce quantitative error plots (ATE, RPE, landmark RMSE)

They are intended for offline evaluation and benchmarking. You should see something like:

Real-Time SLAM Visualization

If you want a real-time animation showing how SLAM evolves step by step (robot motion, particle spread, landmark estimates), you should run:

• slam_gui

This mode focuses on intuition and visualization, not on final quantitative error metrics.

Summary

• Error metrics / evaluation → run
  fastslam_known_correspondences or ekf_known_correspondences

• Real-time SLAM animation → run
  slam_gui

Both modes use the same underlying models but serve different purposes:
evaluation vs. visualization.

Reference

For a detailed explanation of the mathematical derivations, algorithm structure, and experimental results, please refer to the project report included in this repository:

📄 Read the Full Paper (PDF)